Department of IT | REC
CURRICULUM AND SYLLABUS
M.E.SOFTWARE ENGINEERING
REGULATIONS 2017
VISION
To be a Department of Excellence in Information Technology Education, Research and Development
MISSION
To train the students to become highly knowledgeable in the field of Information Technology.
To promote continuous learning and research in core and emerging areas.
To develop globally competent students with strong foundations, who will be able to adapt to
changing technologies.
SEMESTER I
Sl. COURSE COURSE TITLE CATEGORY CONTACT L T P C
No CODE PERIODS
THEORY
Applied Probability And
1. MA17173 Statistics HS 5 3 2 0 4
2. SE17101 Software Requirements PC 3 3 0 0 3
and Design
Advanced Software
3. SE17102 Engineering PC 3 3 0 0 3
4. SE17103 Information Security PC 3 3 0 0 3
Advanced Data
CP17102 Structures and PC
5. 3 3 0 0 3
Algorithms
6. SE17104 Software Architecture PC 3 3 0 0 3
PRACTICAL
Software Requirements
7. SE17111 PC 4 0 0 4 2
and Design Laboratory
TOTAL 24 18 2 4 21
SEMESTER II
Sl. COURSE COURSE TITLE CATEGORY CONTACT L T P C
No CODE PERIODS
THEORY
Software PC
1. SE17201 3 3 0 0 3
Industrialization
Software Testing and PC
2. SE17202 3 3 0 0 3
Quality Assurance
SE17203 Agile Software PE
3. 3 3 0 0 3
Engineering
Data and Visual PC
4. SE17204 3 3 0 0 3
Analytics
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 1
Department of IT | REC
Elective I PE
5. 3 3 0 0 3
Elective II PE
6. 3 3 0 0 3
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 2
Department of IT | REC
PRACTICAL
Software Testing PC
7. SE17211 4 0 0 4 2
Laboratory
Data and Visual PC
8. SE17212 4 0 0 4 2
Analytics Laboratory
Term Paper Writing PC 2
9. CP17212 0 0 2 1
And Seminar
TOTAL 28 18 0 10 23
SEMESTER III
Sl. COURSE COURSE TITLE CATEGORY CONTACT L T P C
No CODE PERIODS
THEORY
Integrated Software PC
1. SE17301 3 3 0 0 3
Project Management
2. Elective III PE 3 3 0 0 3
3. Elective IV PE 3 3 0 0 3
4. Elective V PE 3 3 0 0 3
PRACTICAL
Project Work EEC
5. 12 0 0 12 6
SE17311 (Phase I)
TOTAL 24 12 0 12 18
SEMESTER IV
Sl. COURSE COURSE TITLE CATEGORY CONTACT L T P C
No CODE PERIODS
THEORY
Project Work EEC
1. SE17411 24 0 0 24 12
(Phase II)
PRACTICAL
2.
TOTAL 24 0 0 24 12
TOTAL NO. OF CREDITS: 74
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 3
Department of IT | REC
LIST OF ELECTIVES
SEMESTER II ELECTIVE I & II
Sl. COURSE CONTACT
No CODE COURSE TITLE CATEGORY PERIODS L T P C
PE
1. SE17E21 Software Agents 3 3 0 0 3
Machine Learning PE
2. CP17203 3 3 0 0 3
Techniques
Software Reuse PE
3. SE17E22 3 3 0 0 3
Techniques
SE17E23 Knowledge PE
4. 3 3 0 0 3
Management
SE17E24 Personal Software PE
5. 3 3 0 0 3
Process
SE17E25 Software Test PE
6. 3 3 0 0 3
Automation
SE17E26 Software Verification PE
7. 3 3 0 0 3
and Validation
SE17E27 PE
8. Software Security 3 3 0 0 3
PE
9. CP17E36 Internet of Things 3 3 0 0
3
SEMESTER III ELECTIVE – III, ELECTIVE – IV & ELECTIVE – V
Sl. COURSE CONTACT
No CODE COURSE TITLE CATEGORY PERIODS L T P C
SE17E31 Information Retrieval PE
1. 3 3 0 0 3
Techniques
2. SE17E32 Deep Learning PE 3 3 0 0 3
Software Metrics PE
3. SE17E33 3 3 0 0 3
Web Data Mining PE
4. SE17E34 3 3 0 0 3
Software PE
5. SE17E35 3 3 0 0 3
Documentation
Social Network PE
6. CP17E37 3 3 0 0 3
Analysis
PE
7. SE17E36 Software Reliability 3 3 0 0 3
PE
8. SE17E37 Soft Computing 3 3 0 0 3
PE
9. SE17E38 Business Intelligence 3 3 0 0 3
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 4
Department of IT | REC
SYLLABUS
MA17173 APPLIED PROBABILITY AND STATISTICS L T P C
3 2 0 4
OBJECTIVES:
To understand the concept of random variable and probability distribution for solving problems.
To develop the concept of correlation and regression and apply in real life problems.
To understand the techniques of forecasting.
To develop the skills on decision making using the concept of testing of hypothesis.
UNIT I ONE DIMENSIONAL RANDOM VARIABLES 15
Random variables - Probability function – Moments – Moment generating functions and their properties –
Binomial, Poisson, Geometric, Uniform, Exponential, Gamma and Normal distributions – Functions of a
Random Variable.
UNIT II TWO DIMENSIONAL RANDOM VARIABLES 15
Joint distributions – Marginal and Conditional distributions – Functions of two dimensional random variables
– Regression Curve – Correlation
UNIT III ESTIMATION THEORY 15
Unbiased Estimators – Method of Moments – Maximum Likelihood Estimation - Curve fitting by Principle of
least squares – Regression Lines.
UNIT IV TESTING OF HYPOTHESES 15
Sampling distributions - Type I and Type II errors – Tests based on Normal, t, 2 and F distributions for
testing of mean, variance and proportions – Tests for Independence of attributes and Goodness of fit.
UNIT V MULTIVARIATE ANALYSIS 15
Random Vectors and Matrices - Mean vectors and Covariance matrices – Multivariate Normal density and its
properties - Principal components Population principal components Principal components from standardized
Variables
TOTAL: 75 PERIODS
OUTCOMES:
On completion of the course, students will be able to
1. Use the concept of MGF and probability distribution for solving problems that arise from time to
time.
2. Apply the concept of correlation and regression in real life situation.
3. Apply the concept of estimation theory and curve fitting for forecasting.
4. Enable the students to use the concepts of Testing of Hypothesis for industrial problems
5. Identify and analyze the principle components of different process.
REFERENCES:
1. Veerarajan T, Probability, statistics and random process with queueing theory and queueing networks,
4th edition, McGraw - Hill Publishing Company Limited
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 5
Department of IT | REC
2. Richard A. Johnson and Dean W. Wichern, ―Applied Multivariate Statistical Analysis‖,Pearson
Education, Asia, Fifth Edition.
3. Gupta S.C. and Kapoor V.K.‖Fundamentals of Mathematical Statistics‖, Sultan and Sons.
4. Jay L. Devore, ―Probability and Statistics for Engineering and the Sciences‖, Thomson andDuxbury.
5. Richard Johnson. ‖Miller & Freund‘s Probability and Statistics for Engineer‖, Prentice – Hall,Seventh
Edition, 2007.
6. Dallas E Johnson, ―Applied Multivariate Methods for Data Analysis‖, Thomson and
Duxburypress.
SE17101 SOFTWARE REQUIREMENTS AND DESIGN L T P C
3 0 0 3
OBJECTIVES:
Understand the basics of requirements engineering
Learn different techniques used for requirements elicitation
Know the role played by requirements analysis in requirement integration
Gain knowledge about Dynamic modeling
Understand the importance of Software Quality attributes in Software Design
UNIT I REQUIREMENT ENGINEERING OVERVIEW 9
Software Requirement Overview- Software Development Roles- Software Development Process Kernels-
Commercial Life Cycle Model- Vision Development- Stakeholders Needs & Analysis – Stakeholders Needs-
Stakeholders activities.
UNIT II REQUIREMENT ELICITATION 9
The Process of Requirements Elicitation- Requirements Elicitation Problems- Problems of Scope- Problems
of Understanding- Process of Volatility-Current Elicitation Techniques- Information Gathering-
Requirements Expression & Analysis – Validation- An Elicitation Methodology Framework- A
Requirements Elicitation Process Model- Methodology over Method- Integration of Techniques- Fact-
Finding - Requirements Gathering- Evaluation and Rationalization- Prioritization- Integration and
Validation.
UNIT III REQUIREMENT ANALYSIS 9
Identification of Functional and Non Functional Requirements- Identification of Performance
Requirements- Identification of safety Requirements- Analysis- Feasibility and Internal Compatibility of
System Requirements- Definition of Human Requirements Baseline.
UNIT IV DETAILED DESIGN 9
Dynamic Interaction Modeling – Object Interaction Modeling – Message Sequence Numbering on
Interaction Diagram – Dynamic Interaction Modeling – Stateless Dynamic Interaction Modeling– Finite
State Machines and State Transitions – Events, Guard Conditions and Actions – Hierarchical State charts –
Guidelines for designing State Charts – Steps in State Dependent Dynamic Interaction Modeling.
UNIT V ARCHITECTURAL DESIGN 9
Software Architecture and Component Based Software Architecture – Multiple views of Software
Architecture and Patterns – Documenting Software Architecture – Interface Design – Designing Software
Architecture – Software Sub system Architectural Design – Designing Object oriented Software Architecture
– Designing Component Based Software Architecture
TOTAL: 45 PERIODS
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 6
Department of IT | REC
OUTCOMES:
At the end of the course, the student will be able to:
1. Requirement engineering overview concepts
2. Understanding Requirement gathering.
3. Identified the Requirement Analysis.
4. Apply UML notations in designing software.
5. Gain knowledge about Static and Dynamic modelling.
REFERENCES:
1. Dean Leffingwe, Don Widrig,―Managing Software Requirements A Use Case Approach‖Second
Addition, Addison Wesley, 2003.
2. Ian Graham, ― Requirements Engineering and Rapid Development‖, Addison Wesley, 1998.
3. Ian Sommerville, ―Pete Sawyer, Requirements Engineering: A Good Practice Guide‖, Sixth
Edition, Pearson Education, 2004.
4. Karl Eugene Wiegers, ―Software Requirements‖, Word Power Publishers, 2000.
5. Wiegers, Karl, Joy Beatty, ―Software requirements‖, Pearson Education, 2013.
SE17102 ADVANCED SOFTWARE ENGINEERING L T P C
3 0 0 3
OBJECTIVES:
To have a clear understanding of Software Engineering concepts.
To gain knowledge of the Analysis and System Design concepts.
To learn how to manage change during development.
To known about human computer interaction and reusing pattern.
To learn the SOA and AOP concepts.
UNIT I INTRODUCTION 9
System Concepts – Software Engineering Concepts - Software Life Cycle– Development Activities –
Managing Software Development – Unified Modeling Language – Project Organization – Communication.
UNIT II ANALYSIS 9
Requirements Elicitation – Use Cases – Unified Modeling Language, Tools – Analysis Object Model
(Domain Model) – Analysis Dynamic Models – Non-functional requirements – Analysis Patterns.
UNIT III SYSTEM DESIGN 9
Overview of System Design – Decomposing the system -System Design Concepts – System Design Activities
– Addressing Design Goals – Managing System Design.
UNIT IV IMPLEMENTATION AND MANAGING CHANGE 9
Programming languages and coding- Human computer interaction-Reusing Pattern Solutions – Specifying
Interfaces – Mapping Models to Code – Testing Rationale Management – Configuration Management –
Project Management -real time interface design( eg: mobile design)
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 7
Department of IT | REC
UNIT V ASPECT ORIENTED SOFTWARE DEVELOPMENT 9
AO Design Principles -Separations of Concerns, Subject Oriented Decomposition, Traits, Aspect Oriented
Decomposition, Theme Approach, Designing Base and Crosscutting Themes, Aspect-Oriented Programming
using Aspect-J.
TOTAL: 45 PERIODS
OUTCOMES:
1. A clear understanding of Software Engineering concepts.
2. Knowledge gained of Analysis and System Design concepts.
3. Ability to manage change during development.
4. Basic idea of the SOA and AOP concepts.
5. Gained AO design principles and concepts.
REFERENCES:
1. Bernd Bruegge, Alan H Dutoit, ―Object-Oriented Software Engineering‖, 2nd ed, Pearson Education,
2. 2004.
3. Craig Larman, ―Applying UML and Patterns‖, 3rd ed, Pearson Education, 2005.
4. Stephen Schach, ― Software Engineering ― ,7th ed, McGraw-Hill, 2007.
5. RamnivasLaddad ―AspectJ in Action‖ , Manning Publications, 2003.
6. Robert E. Filman, TzillaElrad, Siobhan Clarke, and Mehmet Aksit, ―Aspect-Oriented Software
Development‖, October 2006.
7. Ivar Jacobson and Pan-Wei Ng , ―Aspect-Oriented Software Development with Use Cases, (The
Addison-Wesley Object Technology Series)‖ , December 2004
8. ―Aspect-Oriented Analysis and Design: The Theme Approach, (The Addison-Wesley Object
Technology Series)‖, Siobhan Clarke and Elisa Baniassad, March 2005.
9. Joseph D. Gradecki and Nicholas Lesiecki ―Mastering Aspect J: Aspect-Oriented Programming in
Java‖ , March 2003.
SE17103 INFORMATION SECURITY L T P C
3 0 0 3
OBJECTIVES:
To learn the core fundamentals of system and web securityconcepts
To have through understanding in the security concepts related tonetworks.
To deploy the security essentials in ITSector.
To be exposed to the concepts of Cyber Security and encryptionConcepts.
To perform a detailed study of Privacy and Storage security and related issues.
UNIT I SYSTEM SECURITY 9
Building a secure organization- A Cryptography primer- detecting system Intrusion- Preventing system
Intrusion- Fault tolerance and Resilience in cloud computing environments- Security web applications,
services and servers.
UNIT II NETWORK SECURITY 9
Internet Security - Botnet Problem- Intranet security- Local Area Network Security -
WirelessNetworkSecurity-WirelessSensorNetworkSecurity-CellularNetworkSecurity-Optical Network
Security- Optical wireless Security.
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 8
Department of IT | REC
UNIT III SECURITY MANAGEMENT 9
Information security essentials for IT Managers- Security Management System - Policy Driven System
Management- IT Security - Online Identity and User Management System - Intrusion and Detection and
Prevention System.
UNIT IV CYBER SECURITY AND CRYPTOGRAPHY 9
Cyber Forensics- Cyber Forensics and Incidence Response - Security e-Discovery - NetworkForensics-
DataEncryption-SatelliteEncryption-Passwordbasedauthenticated
Key establishment Protocols.
UNIT V PRIVACY AND STORAGE SECURITY 9
Privacy on the Internet - Privacy Enhancing Technologies - Personal privacy Policies - Detection of
Conflicts in security policies- privacy and security in environment monitoring systems. Storage Area
Network Security - Storage Area Network Security Devices - Risk
management - Physical Security Essentials.
TOTAL : 45 PERIODS
OUTCOMES:
Upon completion of this course the students should be able to
1. Understand the core fundamentals of systemsecurity
2. Apply the security concepts related to networks in wired and wirelessscenario
3. Implement and Manage the security essentials in ITSector
4. Able to explain the concepts of Cyber Security and encryptionConcepts
5. Able to attain a thorough knowledge in the area of Privacy and Storage security and relatedIssues.
REFERENCES:
1. John R.Vacca, ―Computer and Information Security Handbook‖, Second Edition, Elsevier 2013.
2. Michael E. Whitman, Herbert J. Mattord, ―Principal of Information Security‖, Fourth Edition,
Cengage Learning, 2012.
3. Richard E.Smith,― Elementary Information Security‖, Second Edition, Jones and Bartlett Learning,
2016.
CP17102 ADVANCED DATA STRUCTURES AND ALGORITHMS L T P C
3 0 0 3
OBJECTIVES:
To understand the principles of iterative and recursive algorithms.
To learn the graph search algorithms.
To study network flow and linear programming problems.
To learn the hill climbing and dynamic programming design techniques.
To develop recursive backtracking algorithms.
UNIT I ITERATIVE AND RECURSIVE ALGORITHMS 9
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 9
Department of IT | REC
Iterative Algorithms: Measures of Progress and Loop Invariants-Paradigm Shift: Sequence of Actions versus
Sequence of Assertions- Steps to Develop an Iterative Algorithm-Different Types of Iterative Algorithms--
Typical Errors-Recursion-Forward versus Backward- Towers of Hanoi Checklist for Recursive Algorithms-
The Stack Frame-Proving Correctness with Strong Induction Examples of Recursive Algorithms-Sorting and
Selecting Algorithms-Operations on Integers Ackermann‘s Function- Recursion on Trees-Tree Traversals-
ExamplesGeneralizingtheProblem-HeapSortandPriorityQueues-RepresentingExpressions.
UNIT II OPTIMISATION ALGORITHMS 9
Optimization Problems-Graph Search Algorithms-Generic Search-Breadth-First Search Dijkstra‘s
Shortest-Weighted-Path-Depth-First Search-Recursive Depth-First Search-Linear Ordering of a Partial
Order- Network Flows and Linear Programming-Hill Climbing-Primal Dual Hill Climbing Steepest
Ascent Hill Climbing-Linear Programming-Recursive Backtracking-Developing Recursive Backtracking
Algorithm- Pruning Branches-Satisfiability.
UNIT III DYNAMIC PROGRAMMING ALGORITHMS 9
Developing a Dynamic Programming Algorithm-Subtle Points- Question for the Little Bird Sub instances and
Sub solutions -Set of Substances-Decreasing Time and Space-Number of Solutions-Code. Reductions and
NP - Completeness – Satisfiability - Proving NPCompleteness-3-Coloring- Bipartite Matching.
Randomized Algorithms - Randomness to Hide Worst Cases Optimization Problems with a Random Structure
UNIT IV SHARED OBJECTS AND CONCURRENT OBJECTS 9
Shared Objects and Synchronization -Properties of Mutual Exclusion-The Mora l- The Producer–
Consumer Problem -The Readers–Writers Problem-Realities of Parallelization Parallel Programming-
Principles- Mutual Exclusion-Time- Critical Sections—Thread Solutions-The Filter Lock-Fairness-
Lamport‘s Bakery Algorithm-Bounded Timestamps-Lower Bounds on the Number of Locations-Concurrent
Objects- Concurrency and Correctness Sequential Objects-Quiescent Consistency- Sequential Consistency-
Linearizability- Formal Definitions- Progress Conditions- The Java Memory Model.
UNIT V CONCURRENT DATA STRUCTURES 9
Practice-Linked Lists-The Role of Locking-List-Based Sets-Concurrent Reasoning- Coarse Grained
Synchronization-Fine-Grained Synchronization-Optimistic Synchronization- Lazy Synchronization-Non-
Blocking Synchronization-Concurrent Queues and the ABA Problem Queues-A Bounded Partial Queue-An
Unbounded Total Queue-An Unbounded Lock-Free Queue Memory Reclamation and the ABA Problem-
Dual Data Structures- Concurrent Stacks and Elimination- An Unbounded Lock-Free Stack-
Elimination-The Elimination Backoff Stack.
TOTAL: 45 PERIODS
OUTCOMES:
At the end of the course, the student will be able to:
1. Design and apply iterative and recursive algorithms.
2. Design and implement optimization algorithms in specific applications.
3. Design appropriate shared objects and concurrent objects for applications.
4. Implement and apply concurrent linked lists, stacks, and queues.
REFERENCES:
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 10
Department of IT | REC
1. Jeff Edmonds, ―How to Think about Algorithms‖, Cambridge University Press, 2008.
2. M. Herlihy and N. Shavit, ―The Art of Multiprocessor Programming‖, MorganKaufmann, 2008.
3. Steven S. Skiena, ―The Algorithm Design Manual‖, Springer, 2008.
4. Peter Brass, ―Advanced Data Structures‖, Cambridge University Press, 2008.
5. S. Dasgupta, C. H. Papadimitriou and U. V. Vazirani, ―Algorithms‖,McGraw-Hill,2008.
6. J. Kleinberg and E. Tardos, "Algorithm Design―, Pearson Education, 2006.
7. T. H. Cormen, C. E. Leiserson, R. L. Rivest and C. Stein, ―Introduction to Algorithms―,PHI Learning
Private Limited, 2012.
8. Rajeev Motwani andPrabhakarRaghavan, ―Randomized Algorithms‖, CambridgeUniversity Press,
1995.
9. A. V. Aho, J. E. Hopcroft, and J. D. Ullman, ―The Design and Analysis of ComputerAlgorithms‖,
Addison-Wesley, 1975.
10. A. V. Aho, J. E. Hopcroft, and J. D. Ullman,‖Data Structures and Algorithms‖,Pearson, 2006.
SE17104 SOFTWARE ARCHITECTURE L T P C
3 0 0 3
OBJECTIVES:
To understand the fundamentals of software architecture
To understand various software architecture design components
To identify quality attributes for designing non-functional properties
To develop architectural documentation
To develop appropriate architectures for various Case studies like semantic web services,
supply chain cloud services.
UNIT I INTRODUCTION 9
Architecture business cycle– architectural patterns– reference models– architectural structures,
views–Basic Concepts of Software Architecture
UNIT II ARCHITECTURE DESIGN 9
Typical Architectural Design - Data Flow - Independent Components - Call and Return - Using
Styles in Design – choices of styles – Architectural design space – Theory of Design Spaces –
Design space of Architectural Elements – Design space of Architectural styles..
UNIT III DESIGNING FORNON FUNCTIONAL PROPERTIES 9
Understanding Quality Attributes–Functionality and Architecture–Architecture and Quality
Attributes–System Quality Attributes–Quality attribute Scenarios in Practice-Introducing Tactics–
Availability Tactics–Modifiability Tactics–Performance Tactics-Security Tactics–Testability
Tactics–Usability Tactics–Relationship of Tactics to Architectural Patterns–Architectural Patterns
and Styles
UNIT IV ARCHITECTUREDESCRIPTIONDOCUMENTATIONANDEVALUATION
9
Early Architecture Description Languages–Domain and Style Specific ADLs–
Extensible ADLs-Documenting Software architecture – Architecture Evaluation -ATAM
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Department of IT | REC
UNIT V ASPECT ORIENTED ARCHITECTURES ANDCASESTUDY 9
Aspect Oriented Architectures- AOP in UML, AOP tools, Architectural aspects and middleware
Selection of Architectures, Evaluation of Architecture Designs, Case Study: Online Computer
Vendor, order processing, manufacture &shipping –inventory, supply chain cloud service
Management, semantic web services
TOTAL : 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
1. Explain key architectural drivers
2.Have a good knowledge on various software architecture design components
3. Identify key architectural structures.
4. Adopt good practices for documenting the architecture
5. Have a working knowledge to develop appropriate architectures through various case
studies
TEXTBOOKS:
1. Len Bass, Paul Clements, Rick Kazman, ―Software Architecture in Practice‖, Third
Edition, Addison-Wesley, 2003.
2. Richard N Taylor, NenadMedvidovic and Eric M. Dashofy, ―Software Architecture,
Foundations, Theory and Practice‖, Wiley2010.
REFERENCES:
1. Frank Buschmann, RegineMeunier, Hans Rohnert, Michael Stal, ―Pattern Oriented
Software Architecture‖ , Volume1, 1996.
2. Maryshawand David Garlan, ―Software Architecture–Perspectives on an emerging
discipline‖, Pearson education, 2008.
SE17111 SOFTWARE REQUIREMENTS AND DESIGN LABORATORY L T P C
0 0 4 2
OBJECTIVES:
The students should develop all the necessary requirements based on IEEE standards or any other
standardized standards and should prepare requirement document and design document after
completion.
Use any open source software for requirements elicitation, requirements analysis and requirements
validation.
Use any open source software for performing software design based on the requirements obtained in 2
for each system.
1. ONLINE SHOPPING MALL
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Department of IT | REC
PROJECT DESCRIPTION:
The Online Shopping Mall (OSM) application enables vendors to set up online shops, customers to browse
through the shops, and a system administrator to approve and reject requests for new shops and maintain lists
of shop categories. Also on the agenda is designing an online shopping site to manage the items in the shop
and also help customers purchase them online without having to visit the shop physically. The online
shopping mall will showcase a complete shopping experience in a small package. This project envisages
bridging the gap between the seller, the retailer and the customer. A very high flexibility is being maintained
in the design process so that this project can take the following path: -
A multiple merchant venue with each merchant having his/her own window which the customer can
visit to browse and subsequently buy the products.
Maintaining the deliverable goods as well as services through single or multiple windows is also on
the agenda.
TARGET USERS:
Mall Administrator: The Mall Administrator is the super user and has complete control over all the activities
that can be performed. The application notifies the administrator of all shop creation requests, and the
administrator can then approve or reject them. The administrator also manages the list of available product
categories. The administrator can also view and delete entries in the guestbook.
Shop Owner: Any user can submit a shop creation request through the application. When the request is
approved by the Mall Administrator, the requester is notified, and from there on is given the role of Shop
Owner. The Shop Owner is responsible for setting up the shop and maintaining it. The job involves managing
the sub-categories of the items in the shop. Also, the shop owner can add or remove items from his shop. The
Shop Owner can view different reports that give details of the sales and orders specific to his shop. The Shop
Owner can also decide to close shop and remove it from the mall.
Mall Customer/Guests: A Mall Customer can browse through the shops and choose products to place in a
virtual shopping cart. The shopping cart details can be viewed and items can be removed from the cart. To
proceed with the purchase, the customer is prompted to login. Also, the customer can modify personal profile
information (such as phone number and shipping address) stored by the application. The customer can also
view the status of any previous orders.
Employees:
Purchase department under a Purchase manager to overlook purchasing activities if warehousing
needs arise.
Sales department under a Sales manager who will look after the sale of products and services.
Accounts department under an Accounts manager to look after the accounting activities of the
enterprise.
2. BANKING SYSTEM
PROJECT DESCRIPTION:
A bank has several automated teller machines (ATMs), which are geographically distributed and connected
via a wide area network to a central server. Each ATM machine has a card reader, a cash dispenser, a
keyboard/display, and a receipt printer. By using the ATM machine, a customer can withdraw cash from
either checking or savings account, query the balance of an account, or transfer funds from one account to
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 13
Department of IT | REC
another. A transaction is initiated when a customer inserts an ATM card into the card reader. Encoded on the
magnetic strip on the back of the ATM card are the card number, the start date, and the expiration date.
Assuming the card is recognized, the system validates the ATM card to determine that the expiration date has
not passed, that the user-entered PIN (personal identification number) matches the PIN maintained by the
system, and that the card is not lost or stolen. The customer is allowed three attempts to enter the correct PIN;
the card is confiscated if the third attempt fails. Cards that have been reported lost or stolen are also
confiscated. If the PIN is validated satisfactorily, the customer is prompted for a withdrawal, query, or transfer
transaction. Before withdrawal transaction can be approved, the system determines that sufficient funds exist
in the requested account, that the maximum daily limit will not be exceeded, and that there are sufficient funds
available at the local cash dispenser.
If the transaction is approved, the requested amount of cash is dispensed, a receipt is printed containing
information about the transaction, and the card is ejected. Before a transfer transaction can be approved, the
system determines that the customer has at least two accounts and that there are sufficient funds in the account
to be debited. For approved query and transfer requests, a receipt is printed and card ejected.
A customer may cancel a transaction at any time; the transaction is terminated and the card is ejected.
Customer records, account records, and debit card records are all maintained at the server.
3. CAMPUS MANAGEMENT SYSTEM
PROJECT DESCRIPTION:
The Campus Management System; is fully computerized information organization, storage and retrieval
system that could provide us any information about an Institute just at the click of a mouse. The most
fascinating asset about a computerized College fee Manager is that it enables us to explore any institute
related information at any time on demand and that too in an absolutely user friendly environment that could
be accessed even by a layman very easily.
OBJECTIVES AND GOALS:
To automate the functions at a Higher Education Institute, the main missions of this software are as under
To provide user-friendly interface to the college administrator
To minimize the typing errors during data entry
To search record of a particular object (course, student, faculty etc.)
To update the record of an object
To generate various reports for management
To print various reports
To reduce the typing work by keeping maximum information available on the screen ant to reduce the
expenditure involving stationery items such as paper, ledgers, fee receipt book etc.
To provide consistent, updated and reliable data at any time on demand.
To analyse, plan and forecast the inflation or recession graph of the in college in the near future based
on the college‘s record of revenue sources and expenditure.
To provide the most important feature of maintaining the valuable back-up of the critical data.
To be bestowed with the latest security facilities provided by the modern computerized DBMS.
PROJECT BUILDING BLOCKS:
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Department of IT | REC
Enrolment Management , Portal management , Admissions/Recruiting , Faculty Information, Student
Services, Student Portal, Hostel management, Parking and Security, Student Health, Student Placement
,Campus Incidents, Faculty Portal , Forum Portal , Student Billing, Alumni Portal.
4. AIR TRAFFIC CONTROL SYSTEM
Air traffic control is a closed loop activity in which pilots state the intent by filing flight plans. Controllers
then plan traffic flow based on the total number of flight plans and, when possible, given clearance to pilots to
fly according to their plans. When planning conflicts arise, controllers resolve them by clearing pilots to fly
alternatives to their plans to avoid the conflicts. If unpredicted atmospheric conditions (e.g., wind speed or
direction) or pilot actions cause deviations from conflict-free planned routings, controllers issue clearances for
tactical maneuvers that solve any resultant problem, albeit not necessarily in a way that furthers the pilot's
goal of reaching the planned destination at a certain time.
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 15
Department of IT | REC
PROBLEM FORMULATION:
Design an air traffic control system (ATCS) that is fault tolerant and scalable, according to the specific
requirements listed in the following sections. The primary objective of the ATCS is to provide separation
services for aircraft that are flying in controlled air space, or where poor visibility prevents from maintaining
visual separation. Aircraft are separated from one another and from terrain hazards.
SPECIFIC SOFTWARE REQUIREMENTS:
The requirements of ATCSs include real-time aspects. The ATCS is a "dynamic" real-time system. Its loading
will vary significantly over time, and has no upper bound. Loading scenarios can vary significantly; hence the
average loading of the ATCS is not a highly useful metric for schedulability and other analyses. Although an
upper bound could possibly be imposed artificially, this may not be a cost-effective solution, since pre-
allocation of computing resources for such a worst case would lead to very poor resource utilization. A
dynamic resource management policy is thus preferred.
5. CAFETERIA ORDERING SYSTEM
The Cafeteria Ordering System is a new system that replaces the current manual and telephone processes for
ordering and picking up lunches in the Process Impact cafeteria.
Patron: A Patron is a Process Impact employee at the corporate campus in Tidal Park, Chennai, who wishes
to order meals to be delivered from the company cafeteria. There are about 600 potential Patrons, of which an
estimated 400 are expected to use the Cafeteria Ordering System .Patrons will sometimes order multiple meals
for group events or guests. An estimated 90 percent of orders will be placed using the corporate Intranet, with
10 percent of orders being placed from home. All Patrons have Intranet access from their offices.
Some Patrons will wish to set up meal subscriptions, either to have the same meal to be delivered every day or
to have the day‘s meal special delivered automatically. A Patron must be able to override a subscription for a
specific day.
Cafeteria Staff: The Process Impact cafeteria currently employs about 20 Cafeteria Staff, who will receive
orders from the Cafeteria Ordering System, prepare meals, and package them for delivery, print delivery
instructions, and request delivery. Most of the Cafeteria Staff will need to be trained in the use of the
computer, the Web browser, and the Cafeteria Ordering System.
Menu Manager: The Menu Manager is a cafeteria employee, perhaps the cafeteria manager, who is
responsible for establishing and maintaining daily menus of the food items available from the cafeteria and the
times of day that each item is available. Some menu items may not be available for delivery. The Menu
Manager will also define the cafeteria‘s daily specials. The Menu Manager will need to edit the menus
periodically to reflect planned food items that are not available or price changes.
Meal Deliverer: As the Cafeteria Staff prepare orders for delivery, they will print delivery instructions and
issue delivery requests to the Meal Deliverer, who is either another cafeteria
employee or a contractor. The Meal Deliverer will pick up food and delivery instructions for each meal and
deliver it to the Patron. The Meal Deliverer‘s primary interactions with the system will be to reprint the
delivery instructions on occasion and to confirm that a meal was (or was not) delivered.
TOTAL: 60 PERIODS
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 16
Department of IT | REC
OUTCOMES:
At the end of this course, the students should be able to:
1. Gain clear understanding of Software Engineering concepts.
2. Prepare SRS including the details of requirements engineering.
3. Describe the stages of requirements elicitation.
4. Analyze software requirements gathering.
5. Gain knowledge of Analysis and System Design concepts
SEMESTER II
SE17201 SOFTWARE INDUSTRIALIZATION L T P C
3 0 0 3
OBJECTIVES:
To point out the need for industrialization in softwaredevelopment.
To understand the non functional requirements in software engineering.
To carry out performanceanalyses.
To study the various types ofscalability.
To acquire the art of capacityplanning.
To understand the techniques for infrastructuremanagement.
UNIT I INDUSTRIALIZATION OF SOFTWARE DEVELOPMENT 9
The Fragile Hand Weaving – Features Vs Robustness – Components and Services Based Development –
Agile and Dev Ops - Software Factory – Automation
UNIT II NON FUNCTIONAL REQUIREMENTS AND ENGINEERING 9
NFRs - Cost of Quality – Business and System View – Industrialization Process in SDLC – Performance
and Scalability – Capacity Planning –Production Operations
UNIT III PERFORMANCE AND SCALABILITY ENGINEERING 9
Engineering for Performance and Scalability -Performance Modeling, Measurement and Testing – Workload
Characterization – Latency and Throughput Requirements – Resource Usage Measurements Processor,
Memory, Disk, Network – Performance Testing and Profiling – Bottleneck and Hotspot Identification –
Vertical and Horizontal Scalability – Load, Space and Structural Scalability – Endurance Engineering –
Analysis and Presenting Recommendations – Tools for Performance andScalability
UNIT IV THE ART OF CAPACITY PLANNING 9
Capacity Planning Art Vs Science – Budgetary Capacity Planning - Utilization, Service Demand, The Forced
Flow, Interactive Response Time, Little‗s Laws – Using Queuing Models – Markov Models – M/M/1 M/G/1
Single Queue Systems – Mean Value Analysis- Multi Class Models – Priority Scheduling-Fork/Join Queuing
Networks – Production Capacity Forecasting With Regression and Time Series Models – Tools for
CapacityPlanning
UNIT V PRODUCTION SYSTEMS MANAGEMENT 9
Infrastructure Management and Support – Systems, Storage and Network Monitoring – High Availability –
Service Levels – Change and Configuration Management- Capacity Augmentation - Modernizing and Cloud
Enablement - Automation
TOTAL: 45 PERIODS
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 17
Department of IT | REC
OUTCOMES:
At the end of this course, the students will be able to
1. Understand SOA and DevOps
2. Understand the non-functional requirements in softwareengineering
3. Apply various performance analysis techniques
4. Analyze software systems for scalability and Planning methods
5. Apply infrastructure managementtechniques
REFERENCES:
1. Andre B.Bondi, ―Foundations of Software and System Performance Engineering‖, Addison
Wesley,2015.
2. Daniel A.Menasce, Dowdy, Almeida, ―Computer Capacity Planning by Example‖,Prentice Hall,2004.
3. L. Chung, B. Nixon, E. Yu and J.Mylopoulos, ―Non-Functional Requirements in Software
Engineering‖, Springer, 2000.
4. Rich Schiesser, ―IT Systems Management‖, Pearson Education, 2010.
SE17202 SOFTWARE TESTING AND QUALITY ASSURANCE L T P C
3 0 0 3
OBJECTIVES :
The student should be able to
Know what is software and the usage of different types of softwares.
Know the Quality Metrics of various Softwares.
Know the methodologies in making Software.
Test the product finally to check the product Quality.
UNIT I INTRODUCTION 9
Introduction to Software Quality - Challenges – Objectives – Quality Factors – Components of SQA –
Contract Review – Development and Quality Plans – SQA Components in Project Life Cycle – SQA Defect
Removal Policies – Reviews.
9
UNIT II TESTING TECHNIQUES AND LEVELS OF TESTING
Using White Box Approach to Test design - Static Testing Vs. Structural Testing – Code Functional Testing
– Coverage and Control Flow Graphs –Using Black Box Approaches to Test Case Design – Random Testing
– Requirements based testing –Decision tables –State-based testing – Cause-effect graphing – Error guessing
– Compatibility testing – Levels of Testing - Unit Testing - Integration Testing - Defect Bash Elimination.
System Testing - Usability and Accessibility Testing – Configuration Testing - Compatibility Testing - Case
study for White box testing and Black box testing techniques.
UNIT III TEST STRATEGIES 9
Testing Strategies – White Box and Black Box Approach – Integration Testing – System and Acceptance
Testing – Performance Testing – Regression Testing - Internationalization Testing – Ad-hoc Testing –
Website Testing – Usability Testing – Accessibility Testing.
UNIT IV TEST AUTOMATION AND MANAGEMENT 9
Test plan – Management – Execution and Reporting – Software Test Automation – Automated Testing tools
- Hierarchical Models of Software Quality – Configuration Management – Documentation Control.
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 18
Department of IT | REC
UNIT V SQA IN PROJECT MANAGEMENT 9
Project progress control – costs – quality management standards – project process standards – management
and its role in SQA – SQA unit.
TOTAL: 45 PERIODS
OUTCOMES:
At the end the student will be able to
1. Analyse the product Quality.
2. Use various testing methods.
3. Assess Quality standards.
4. Applied Quality Metrics for various Softwares.
5. Learnt about various methodologies about software.
Test the product finally to check the product Quality.
REFERENCES :
1. Daniel Galin, ―Software Quality Assurance – from Theory to Implementation‖, Pearson
Education, 2009
2. Yogesh Singh, "Software Testing", Cambridge University Press, 2012
3. AdityaMathur, ―Foundations of Software Testing‖, Pearson Education, 2008
4. Ron Patton, ―Software Testing‖ , Second Edition, Pearson Education, 2007
5. SrinivasanDesikan, Gopalaswamy Ramesh, ―Software Testing – Principles and Practices‖,
Pearson Education, 2006
6. Alan C Gillies, ―Software Quality Theory and Management‖, Cengage Learning, Second Edition,
2003.
7. Robert Furtell, Donald Shafer, and Linda Shafer, "Quality Software Project Management",
Pearson Education Asia, 2002.
8. William Perry, ―Effective Methods of Software Testing‖, Third Edition, Wiley Publishing 2007
9. SrinivasanDesikan and Gopalaswamy Ramesh, ―Software Testing – Principles and Practices‖,
Pearson Education, 2007
SE17203 AGILE SOFTWARE ENGINEERING L T P C
3 0 0 3
OBJECTIVES:
Understand agile software development practices
Demonstrate Agile development and testingtechniques
To learn about agile knowledge management
Know the benefits and pitfalls of working in an Agileteam
Understand agile development and testing
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 19
Department of IT | REC
9
UNIT-I AGILE METHODOLOGY
Theories for Agile management – agile software development – traditional model vs. agile model -
classification of agile methods – agile manifesto and principles – agile project management – agile team
interactions – ethics in agile teams - agility in design, testing – agile documentations – agile drivers,
capabilities and values.
9
UNIT II AGILE PROCESSES
Lean production - SCRUM, Crystal, Feature Driven Development, Adaptive Software Development, and
Extreme Programming: Method overview – lifecycle – work products, roles and practices.
9
UNIT III AGILITY AND KNOWLEDGE MANAGEMENT
Agile information systems – agile decision making –Early‘s schools of KM – institutional knowledge
evolution cycle – development, acquisition, refinement, distribution, deployment , leveraging – KM in
software engineering – managing software knowledge – challenges of migrating to agile methodologies –
agile knowledge sharing – role of story-cards – Story-card Maturity Model (SMM).
AGILITY AND REQUIREMENTS ENGINEERING 9
UNIT IV
Impact of agile processes in RE – current agile practices – variance – overview of RE using agile –
managing unstable requirements – requirements elicitation – agile requirements abstraction model –
requirements management in agile environment, agile requirements prioritization – agile requirements
modeling and generation – concurrency in agile requirements generation.
UNIT V AGILITY AND QUALITY ASSURANCE 9
Agile Interaction Design - Agile product development – Agile Metrics – Feature Driven Development
(FDD) – Financial and Production Metrics in FDD – Agile approach to Quality Assurance - Test Driven
Development – Pair programming: Issues and Challenges - Agile approach to Global Software
Development.
TOTAL : 45PERIODS
OUTCOMES:
At the end of this course, the students should be ableto:
1. The know importance of interacting with business stakeholders in determining the requirements
for a softwaresystem.
2. Apply iterative software development process
3. Apply the impact of social aspects on software development success.
4. Learnt about Quality metrics of various Softwares.
5. Applied various methodologies for making software.
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 20
Department of IT | REC
REFERENCES:
1. CraigLarman,―Agile and Iterative Development:A manager‗sGuide‖, Addison-Wesley,2004
2. DavidJ.Anderson;EliSchragenheim,―AgileManagementforSoftwareEngineering:Applying the
Theory of Constraints for Business Results‖, Prentice Hall,2003
3. Dingsoyr,Torgeir,Dyba,Tore,Moe,NilsBrede(Eds.),―AgileSoftwareDevelopment,Current Research
and Future Directions‖, Springer-Verlag Berlin Heidelberg,2010
4. Hazza&Dubinsky, ―Agile Software Engineering, Series: Undergraduate Topics in Computer
Science‖, Springer, VIII edition, 2009
5. Kevin C. Desouza, ―Agile information systems: conceptualization, construction, and management‖, Butterworth-Heinemann,2007.
SE17204 DATA AND VISUAL ANALYTICS L T P C
3 0 0 3
OBJECTIVES:
To develop skills to both design and critique visualizations.
To introduce visual perception and core skills for visual analysis.
To understand visualization for time-series analysis.
To understand visualization for ranking analysis, deviation analysis and distribution analysis.
To understand visualization for correlation analysis and multivariate analysis.
To understand issues and best practices in information dashboard design.
UNIT I CORE SKILLS FOR VISUAL ANALYSIS 9
Information visualization – effective data analysis – traits of meaningful data – visual perception –making
abstract data visible – building blocks of information visualization – analytical interaction – analytical
navigation – optimal quantitative scales – reference lines and regions – trellises and crosstabs – multiple
concurrent views – focus and context – details on demand – over-plotting reduction – analytical patterns –
pattern examples.
UNIT II TIME-SERIES, RANKING, AND DEVIATION ANALYSIS 9
Time-series analysis – time-series patterns – time-series displays – time-series best practices – part-to-whole
and ranking patterns – part-to-whole and ranking displays – best practices – deviation analysis – deviation
analysis displays – deviation analysis best practices.
UNIT III DISTRIBUTION, CORRELATION, AND MULTIVARIATE NALYSIS 9
Distribution analysis – describing distributions – distribution patterns – distribution displays – distribution
analysis best practices – correlation analysis – describing correlations – correlation patterns – correlation
displays – correlation analysis techniques and best practices – multivariate analysis – multivariate patterns –
multivariate displays – multivariate analysis techniques and best practices.
UNIT IV INFORMATION DASHBOARD DESIGN 9
Information dashboard – Introduction– dashboard design issues and assessment of needs – Considerations for
designing dashboard-visual perception – Achieving eloquence
UNIT V INFORMATION DASHBOARD DESIGN 9
Advantages of Graphics _Library of Graphs – Designing Bullet Graphs – Designing Spark lines – Dashboard
Display Media –Critical Design Practices – Putting it all together Unveiling the dashboard
TOTAL: 45 PERIODS
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 21
Department of IT | REC
OUTCOMES:
Upon completion of the course, the students should be able to:
1. Explain principles of visual perception
2. Apply core skills for visual analysis
3. Apply visualization techniques for various data analysis tasks
4. Design information dashboard
REFERENCES:
1. Ben Fry, "Visualizing data: Exploring and explaining data with the processing environment",
O'Reilly, 2008.
2. Edward R. Tufte, "The visual display of quantitative information", Second Edition, Graphics Press,
2001.
3. Evan Stubbs, "The value of business analytics: Identifying the path to profitability", Wiley, 2011.
4. Gert H. N. Laursen and JesperThorlund, "Business Analytics for Managers: Taking business
intelligence beyond reporting", Wiley, 2010.
5. Nathan Yau, "Data Points: Visualization that means something", Wiley, 2013.
6. Stephen Few, "Information dashboard design: Displaying data for at-a-glance monitoring", second
edition, Analytics Press, 2013.
7. Stephen Few, "Now you see it: Simple Visualization techniques for quantitative analysis", Analytics
Press, 2009.
8. Tamara Munzner, Visualization Analysis and Design, AK Peters Visualization Series, CRC Press,
Nov. 2014.
SE17211 SOFTWARE TESTING LABORATORY L T P C
0 0 4 2
CASE STUDY 1
Cause Effect Graph Testing for a Triangle Program
Perform cause effect graph testing to find a set of test cases for the following program specification: Write a
program that takes three positive integers as input and determine if they
represent three sides of a triangle, and if they do, indicate what type of triangle it is. To be more specific, it
should read three integers and set a flag as follows:
If they represent a scalene triangle, set it to 1.
If they represent an isosceles triangle, set it to 2.
If they represent an equilateral triangle, set it to 3.
If they do not represent a triangle, set it to 4.
CASE STUDY 2
Boundary Value Analysis for a Software Unit
The following is a specification for a software unit. The unit computes the average of 25 floating point
numbers that lie on or between bounding values which are positive values from
1.0 (lowest allowed boundary value) to 5000.0 (highest allowed boundary value). The bounding values and
the numbers to average are inputs to the unit. The upper bound must be greater than the lower bound. If an
invalid set of values is input for the boundaries an error message appears and the user is reported. If the
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 22
Department of IT | REC
boundary values are valid the unit computes the sum and the average of the numbers on and within the
bounds. The average and sum are output by the unit, as well as the total number of inputs that lie within the
boundaries. Derive a set of equivalence classes for the averaging unit using the specification, and
complement the classes using boundary value analysis. Be sure to identify valid and invalid classes. Design a
set of test cases for the unit using your equivalence classes and boundary values. For each test case, specify
the equivalence classes covered, input values, expected outputs, and test case identifier. Show in tabular form
that you have covered all the classes and boundaries. Implement this module in the programming language of
your choice. Run the module with your test cases and record the actual outputs. Save an uncorrected version
of the program for future use.
CASE STUDY 3
Cyclomatic Complexity for Binary Search
Draw a control flow graph for the given binary search code and clearly label each node so that it is linked to
its corresponding statement. Calculate its cyclomatic complexity.
intbinsearch (intx,int v[], int n)
{
int low, high, mid;
low =0;
high = n-1;
while (low <=high) {
mid =(low+high)/2
if (x < v[mid]
high = mid–1;
else if (x > v[mid])
low = mid+1;
else /* found match*/
return mid;
}
return–1; /* no match*/
}
CASE STUDY 4
Data Flow Testing for Gregorian Calendar
A program was written to determine if a given year in the Gregorian calendar is a leap year. The well-known
part of the rule, stipulating that it is a leap year if it is divisible by 4, is implemented correctly in the program.
The programmer, however, is unaware of the exceptions: A centenary year, although divisible by 4, is not a
leap year unless it is also divisible by 400. Thus, while year 2000 was a leap year, the years 1800 and 1900
were not. Determine if the following test-case selection criteria are reliable or valid.
(a) C1(T ) ≡ (T = {1, 101, 1001, 10001})
(b) C2(T ) ≡ (T = {t|1995 ≥ t ≥ 2005})
(c) C3(T ) ≡ (T = {t|1895 ≥ t ≥ 1905})
(d) C4(T ) ≡ (T = {t} ∧ t ∋ {400, 800, 1200, 1600, 2000, 2400})
(e) C5(T ) ≡ (T = {t, t + 1, t + 2, t + 3, t + 4} ∧ t ∋ {100, 200, 300, 400,500})
(f) C6(T ) ≡ (T = {t, t + 1, t + 2, . . . , t + 399} ∧ t ∋ D)
(g) C7(T ) ≡ (T = {t1, t2, t3} ∧ t1, t2, t3 ∋ D)
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 23
Department of IT | REC
CASE STUDY 5
State based Testing for an Assembler
Suppose you were developing a simple assembler whose syntax can be described as follows :
<statement_ :: = <label field><op code><address>
<label field> :: = ‗‗none‘‘ | <identifier> :
<op code> :: = MOVE | JUMP
<address> :: = <identifier> | <unsigned integer>
A stream of tokens is input to the assembler. The possible states for such an assembler are: S1, pre label; S2,
label; S3, valid op code; S4, valid address; S5, valid numeric address. Start, Error, and Done. A table that
describes the inputs and actions for the assembler is as follows:
Inputs Actions
no more tokens A1: Put the label in the symbol table.
Identifier A2: Look up the op code and store its binary value in op code field.
MOVE, JUMP A3: Look up symbol in symbol table and store its value in address field.
colon A4: Convert number to binary, and store that value in address field.
Integer A5: Place instruction in the object module, and print a line in the listing.
A6: Print error message and put all zeroes in the instruction.
Using this information and any assumptions you need to make, develop a state transition diagram for the
assembler. From the state transition diagram develop a set of test cases that will cover all of the state
transitions. Be sure to describe the exact sequence of inputs as well as the expected Sequence of state changes
and actions.
CASE STUDY 6
Stress Testing of a Map-Aided Vehicle Tracking and Scheduling System
The American package courier and freight business faced the double pressures of consolidation and
unstoppable increases in fuel costs. In mid-2008, pump prices were already double those prevailing in early
2007. As well, the recent decision of long-time price leader DHL to ―co-locate‖ dozens of routes with
erstwhile competitor UPS revealed just how fragile are market positions built through decades of promotions.
In Omaha, regional freight leader Red Ball Trucking1 was keener than most to maximize operating
efficiencies out of its substantial fleet of trucks and vans and thereby maintain margins in the face of low-cost
rivals. In March 2008, a brand-new map-based adjunct to the company‘s proprietary logistics and routing
system neared rollout. Extensive ―white box‖, line-by-line testing had eliminated most ofthe gross errors but
the Red Ball CEO was concerned about the scalability of the program test bed. Find out whether the map-
enhanced vehicle tracking and scheduling system would remain stable at benchmarks of 50, 100 and 1000
concurrent users. Clean up any remaining bugs not caught by in-house.
CASE STUDY 7
Model Based Testing
Design and develop a scientific calculator program using various GUI components and events. Build the test
model for the same. Determine the inputs that can be given to the model. Calculate expected output for the
model. Run the test cases. Compare the actual output with the expected output. Any model based technique
can be used for building the test model.
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 24
Department of IT | REC
CASE STUDY 8
Web Application Testing for Student Grade System
With educational organizations under increasing pressure to improve their performance to secure funding for
future provision of programmes, it is vital that they have accurate, up-to-date information. For this reason,
they have MIS systems to record and track student enrolment and results on completion of a learning
programme. In this way they can monitor achievement statistics. All student assignment work is marked and
recorded by individual module tutors using a spreadsheet, or similar, of their own design. In the computing
department these results are input into a master spreadsheet to track a student‘s overall progress throughout
their programme of study. This is then made available to students through the web portal used in college.
Perform web application testing for this scenario.
SE17212 DATA AND VISUAL ANALYTICS LABORATORY L T P C
0 0 4 2
OBJECTIVES:
Be familiar with the algorithms of data mining.
Be acquainted with the tools and techniques used for Knowledge Discovery in Databases.
Be exposed to web mining and text mining.
To known about Hierarchical Clustering algorithm.
To explore Support Vector Machines.
LIST OF EXPERIMENTS:
1. Creation of a Data Warehouse.
2. Apriori Algorithm.
3. FP-Growth Algorithm.
4. K-means clustering.
5. One Hierarchical clustering algorithm.
6. Bayesian Classification.
7. Decision Tree.
8. Support Vector Machines.
9. Applications of classification for web mining.
10. Case Study on Text Mining or any commercial application.
TOTAL: 45 PERIODS
OUTCOMES:
After completing this course, the student will be able to:
1. Apply data mining techniques and methods to large data sets.
2. Use data mining tools.
3. Compare and contrast the various classifiers.
4. Learnt about Hierarchical Clustering algorithm.
5. Gained knowledge about Support Vector Machines.
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 25
Department of IT | REC
CP17212 TERM PAPER WRITING AND SEMINAR L T P C
0 0 2 1
In this course, students will develop their scientific and technical reading and writing skills that they need to
understand and construct research articles. A term paper requires a student to obtain information from a
variety of sources (i.e., Journals, dictionaries, reference books) and then place it in logically developed ideas.
The work involves the following steps:
1. Selecting a subject, narrowing the subject into a topic.
2. Stating an objective.
3. Collecting the relevant bibliography (atleast 15 journal papers)
4. Preparing a working outline.
5. Studying the papers and understanding the author‘s contributions and critically analysing each paper.
6. Preparing a working outline.
7. Linking the papers and preparing a draft of the paper.
8. Preparing conclusions based on the reading of all the papers.
9. Writing the Final Paper and giving final Presentation Please keep a file where the work carried out by
you is maintained. Activities to be carried out.
Activity Instructions Submission Evaluation
week
nd
Selection of area of You are requested to select an area 2 week 3 % Based on clarity
interest and Topic of interest, topic and state an of thought, current
Stating an in writing objective relevance and clarity
Objective in writing
rd
Collecting Information 1. List 1 Special Interest Groups or 3 week 3% ( the selected
about your area & topic professional society information must be
2. List 2 journals area specific and of
3. List 2 conferences, symposia or international and
workshops national standard)
4. List 1 thesis title
5. List 3 web presences (mailing
lists, forums, news sites)
6. List 3 authors who publish
regularly in your area
7. Attach a call for papers (CFP)
from your area.
th
Collection of Journal You have to provide a 4 week 6% ( the list of
papers in the topic in the complete list of references standard papers and
context of the objective – you will be using- Based on reason for selection)
collect 20 & then filter your objective -Search
various digital libraries and
Google Scholar When
picking papers to read - try
to:
Pick papers that are related
to each
other in some ways and/or
that are in the same field so
that you can write a
meaningful survey out of
them, Favour papers from
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 26
Department of IT | REC
well-known
journals and conferences,
Favour ―first or
―foundational papers in the
field (as indicated in other
people‗s survey paper),
Favour more recent papers,
Pick a recent survey of the
field so you can quickly
gain an overview,
Find relationships with
respect to each other and to
your topic area
(classification
scheme/categorization)
Mark in the hard copy of
papers whether complete
work or section/sections of
the paper are being
considered
th
Reading and notes for Reading Paper Process 5 week 8% ( the table given
first 5 papers For each paper form a Table should indicate your
answering the following understanding of the
questions: paper and the
What is the main topic of evaluation is based on
the article? your conclusions
What was/were the main about each paper)
issue(s) the author said they
want to discuss?
Why did the author claim it
was important?
How does the work build on
other‗s work, in the author‗s
opinion?
What simplifying
assumptions does the author
claim to be making?
What did the author do?
How did the author claim
they were going to evaluate
their work and compare it to
others?
What did the author say
were the limitations of their
research?
What did the author say
were the important
directions for future
research?
Conclude with limitations/issues not
addressed by the paper ( from the
perspective of your survey)
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 27
Department of IT | REC
th
Reading and notes for Repeat Reading Paper Process 6 week 8% ( the table given
next 5 papers should indicate your
understanding of the
paper and the
evaluation is based on
your conclusions
about each paper)
th
Reading and notes for Repeat Reading Paper Process 7 week 8% ( the table given
final 5 papers should indicate your
understanding of the
paper and the
evaluation is based on
your conclusions
about each paper)
th
Draft outline 1 and Prepare a draft Outline, your survey 8 week 8% ( this component
Linking papers goals, along with a classification / will be evaluated
categorization diagram based on the linking
and classification
among the papers)
th
Abstract Prepare a draft abstract and give a 9 week 6% (Clarity, purpose
presentation and conclusion) 6%
Presentation & Viva
Voce
th
Introduction Background Write an introduction and 10 week 5% ( clarity)
background sections
th
Sections of the paper Write the sections of your paper 11 week 10% (this component
based on the classification / will be evaluated
categorization diagram in keeping based on the linking
with the goals of your survey and classification
among the papers)
th
Your conclusions Write your conclusions and future 12 week 5% ( conclusions –
work clarity and your
ideas)
th
Final Draft Complete the final draft of your 13 week 10% (formatting,
paper English, Clarity and
linking) 4%
Plagiarism Check
Report
th
th
Seminar A brief 15 slides on your paper 14 & 15 week 10% (based on
presentation and
Viva-voce)
TOTAL: 30 PERIODS
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 28
Department of IT | REC
SEMESTER III
SE17301 INTEGRATED SOFTWARE PROJECT MANAGEMENT L T P C
3 0 0 3
OBJECTIVES:
Understand the basic concept of project management.
Learn the various costing and life cycle management.
Understand the role played by risk in software project.
Appreciate the use of metrics for software project management.
Know the challenges in people management.
UNIT I PROJECT MANAGEMENT & COSTING 9
Software Project Management approaches – Project Acquisition – Initiation – Planning – PERT Execution
and Control – CPM – Change Management – Project Closure – Agile SPM Problems in Software Estimation
– Algorithmic Cost Estimation Process, Function Points, COCOMO II (Constructive Cost Model) –
Estimating Web Application Development – Concepts of Finance, Activity Based Costing and Economic
Value Added (EVA) – Balanced Score Card.
UNIT II PROCESS MODELS & LIFECYCLE MANAGEMENT 9
Software Engineering Process Models - Adaptive Software Development (ASD) - DSDM - SCRUM – Crystal
-Feature Driven Development (FDD) - ISO 9000: 2000 - SPICE – SIX SIGMA – CMMI. SLIM (Software
Life cycle Management) – PLM (Product Lifecycle Management) – PDM (Product Data Management) -
PLM, PDM Applications – Pre-PLM Environment – Change Management.
UNIT III RISK MANAGEMENT 9
Perspectives of Risk Management - Risk Definition – Risk Categories – Risk Assessment: Approaches,
techniques and good practices – Risk Identification / Analysis / Prioritization – Risk Control (Planning /
Resolution / Monitoring) – Risk Retention – Risk Transfer - Failure Mode and Effects Analysis (FMEA) –
Operational Risks – Supply Chain Risk Management.
UNIT IV METRICS 9
Need for Software Metrics – scope – basics – framework for software measurement - Classification of
Software Metrics: Product Metrics (Size Metrics, Complexity Metrics, Halsteads Product Metrics, Quality
Metrics), and Process metrics (Empirical Models, Statistical Models, Theory-based Models, Composite
Models, and Reliability Models) – measuring internal and external product attributes.
UNIT V PEOPLE MANAGEMENT 9
Leadership styles – Developing Leadership skills – Leadership assessment – Motivating People –
Organizational strategy – Management – Team building – Delegation – Art of Interviewing People - Team
Management – Rewarding - Client Relationship Management.
TOTAL: 45 PERIODS
OUTCOMES:
At the end the student will be able to identify the various elements of software management process
framework
1. Use available open source estimation tools for cost estimation
2. Identify existing risk and perform risk assessment
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 29
Department of IT | REC
3. Design a software metric for software project management
4. Modify the art of interviewing people for a given scenario.
REFERENCES:
1. MuraliChemuturi, Thomas M. Cagley, ―Mastering Software Project Management: Best Practices, Tools
and Techniques‖, J. Ross Publishing, 2010
2. Stark, John, ―Decision Engineering: Product Lifecycle Management:21st Century Paradigm for Product
Realisation‖,2ndEdition.,Springer London,2011
3. Antonio Borghesi, Barbara Gaudenzi, ―Risk Management: How to Assess, Transfer and Communicate
Critical Risks: Perspectives in Business Culture‖,Illustrated Edition, Springer, 2012
4. Norman Fenton, James Bieman, ―Software Metrics: A Rigorous and Practical Approach‖, 3rd edition,
CRC Press, 2015.
SEMESTER II
ELECTIVE –I & ELECTIVE-II
SE17E21 SOFTWARE AGENTS L T P C
3 0 0 3
OBJECTIVES:
Have an overview of the agent systems and softwareagents.
Understand the basic concepts of intelligent softwareagents.
Design and build a multi-agentsystem.
Have a basic understanding about software agent technology and to be familiar with some of
the communicating languages, standardization andapplications.
Learn the use of software agents to represent and share information to coordinate activities of
the agents for the purpose of group problemsolving.
UNIT I AGENTS OVERVIEW 9
Agent Definition – Agent Programming Paradigms – Agent Vs Object – Aglet – Mobile Agents –
Agent Frameworks – Agent Reasoning.
UNIT II JAVA AGENTS 9
Processes – Threads – Daemons – Components – Java Beans – ActiveX – Sockets – RPCs –
Distributed Computing – Aglets Programming – Jini Architecture – Actors and Agents – Typed and
proactive messages.
UNIT III MULTI AGENT SYSTEMS 9
Interaction between agents – Reactive Agents – Cognitive Agents – Interaction protocols – Agent
oordination – Agent negotiation – Agent Cooperation – Agent Organization – Self-Interested agents in
Electronic Commerce Applications.
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 30
Department of IT | REC
UNIT IV INTELLIGENT SOFTWARE AGENTS 9
Interface Agents – Agent Communication Languages – Agent Knowledge Representation – Agent
Adaptability – Belief Desire Intension – Mobile Agent Applications.
UNIT V AGENTS AND SECURITY 9
Agent Security Issues – Mobile Agents Security – Protecting Agents against Malicious Hosts –
Untrusted Agent – Black Box Security – Authentication for agents – Security issues for Aglets.
TOTAL : 45PERIODS
OUTCOMES:
At the end of this course, the students should be able to:
1. Create / develop an agent based system for a particulartask.
2. Design an application that uses different security issues for intelligentagents.
3. Effectively apply agent-based technologies in the development and application of distributed
information systems that use softwareagents.
REFERENCES:
1. Bigus&Bigus, " Constructing Intelligent agents with Java ", Wiley,1997
2. Bradshaw, " Software Agents ", MIT Press,2010
3. Gerhard Weiss, ―Multi Agent Systems – A Modern Approach to Distributed Artificial
Intelligence, MIT Press, 2000.
4. Richard Murch, Tony Johnson, "Intelligent Software Agents", Prentice Hall,2000
5. Russel, Norvig, "Artificial Intelligence: A Modern Approach", Second Edition, Pearson
Education,2003
CP17203 MACHINE LEARNING TECHNIQUES L T P C
3 0 0 3
OBJECTIVES:
To understand the machine learning theory
To implement linear and non-linear learning models
To implement distance-based clustering techniques
To build tree and rule based models
To apply reinforcement learning techniques
UNIT I FOUNDATIONS OF LEARNING 9
Components of learning – learning models – geometric models – probabilistic models – logic models –
grouping and grading – learning versus design – types of learning – supervised – unsupervised –
reinforcement – theory of learning – feasibility of learning – error and noise – training versus testing – theory
of generalization – generalization bound – approximation generalization trade off – bias and variance –
learning curve.
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 31
Department of IT | REC
UNIT II LINEAR MODELS 9
Linear classification – univariate linear regression – multivariate linear regression – regularized
regression – Logistic regression – perceptrons – multilayer neural networks – learning neural networks
structures – support vector machines – soft margin SVM – going beyond linearity – generalization and over
fitting – regularization – validation.
UNIT III DISTANCE-BASED MODELS 9
Nearest neighbor models – K-means – clustering around medoids – silhouttes – hierarchical clustering – k-d
trees – locality sensitive hashing – non-parametric regression – ensemble learning– bagging and random
forests – boosting – meta learning.
UNIT IV TREE AND RULE MODELS 9
Decision trees – learning decision trees – ranking and probability estimation trees – regression trees –
clustering trees – learning ordered rule lists – learning unordered rule lists – descriptive rule learning –
association rule mining – first-order rule learning.
UNIT V REINFORCEMENT LEARNING 9
Passive reinforcement learning – direct utility estimation – adaptive dynamic programming – temporal-
difference learning – active reinforcement learning – exploration – learning an action utility function –
Generalization in reinforcement learning – policy search – applications in game playing – applications in
robot control.
TOTAL: 45 PERIODS
OUTCOMES:
At the end of the course, the student will be able to:
1. To explain theory underlying machine learning
2. To construct algorithms to learn linear and non-linear models
3. To implement data clustering algorithms
4. To construct algorithms to learn tree and rule-based models
5. To apply reinforcement learning techniques
REFERENCES:
1. Y. S. Abu-Mostafa, M. Magdon-Ismail, and H.-T. Lin, ―Learning from Data‖,AMLBook
Publishers, 2012.
2. P. Flach, ―Machine Learning: The art and science of algorithms that make sense of data‖, Cambridge
University Press, 2012.
3. K. P. Murphy, ―Machine Learning: A probabilistic perspective‖, MIT Press, 2012.
4. C. M. Bishop, ―Pattern Recognition and Machine Learning‖, Springer, 2007.
5. D. Barber, ―Bayesian Reasoning and Machine Learning‖, Cambridge University Press, 2012.
6. M. Mohri, A. Rostamizadeh, and A. Talwalkar, ―Foundations of Machine Learning‖, MIT Press,
2012.
7. T. M. Mitchell, ―Machine Learning‖, McGraw Hill, 1997.
8. S. Russel and P. Norvig, ―Artificial Intelligence: A Modern Approach‖, Third Edition, Prentice
Hall, 2009.
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 32
Department of IT | REC
SE17E22 SOFTWARE REUSE TECHNIQUES L T P C
3 0 0 3
OBJECTIVES:
To understand the software reuse, object oriented software engineering.
To learn design patterns and factory methods.
To study the structural and behavioural patterns.
To analyse the view handle and client dispatcher.
To study layers, pipes and filters.
UNIT I INTRODUCTION 9
Software reuse success factors, Reuse driven software engineering business, Object oriented software
engineering, applications and component sub systems, use case components, object components.
UNIT II DESIGN PATTERNS 9
Design Patterns – Introduction, Creational patterns, factory, factory method, abstract factory, singleton,
builder prototype.
UNIT III STRUCTURAL & BEHAVIOURAL PATTERNS 9
Structural Patterns- Adapters, bridge, composite, decorator, façade, flyweight, proxy.
Behavioral Patterns – Chain of responsibility, command, interpreter.
UNIT IV HANDLE & CLIENT DISPATCHER 9
Behavioral Patterns – Iterator, mediator, memento, observer,stazte, strategy, template, visitor, other, design
patterns- Whole part, master- slave, view handler, forwarder- receiver, client – dispatcher- server, publisher –
subscriber.
UNIT-V LAYERS, PIPES & FILTERS 9
Architectural patterns – Layers, pipes and filters, black board, broker, model - view controller, presentation-
abstraction – control, micro kernel, reflection.
TOTAL: 45 PERIODS
OUTCOMES:
At the end of the course, the student will be able to:
1. To apply object oriented software engineering
2. To design patterns and build prototype.
3. To implement structural patterns
4. To construct behavioral patterns
5. To apply architectural patterns and micro kernel.
References:
1. Ivar jacabson, Martin Griss, Patrick Hohson – Software Reuse. Architecture, Process and Organization
for Bussiness Success, ACM Press, 1997.
2. Erich Gamma, Richard Helm, Ralph Johnson, John Vlissides – Design Patterns- Addison, 1995, Pearson
Education.
3. Frank Buschmann etc. – Pattern Oriented Software Architecture – Volume 1, Wiley 1996.
4. James W Cooper – Java Design Patterns, a tutorial, Addison 2000, Pearson Education.
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 33
Department of IT | REC
SE17E23 KNOWLEDGE MANAGEMENT L T P C
3 0 0 3
OBJECTIVES:
Learn knowledge engineering basics
Know the knowledge models
Know the techniques of knowledge management and implementation
Learn the knowledge elicitation techniques
Learn scope of knowledge management in project management.
UNIT I INTRODUCTION 9
The value of Knowledge – Knowledge Engineering Basics – Knowledge Economy – The Task and
Organizational Content – Knowledge Management – Knowledge Management Ontology.
(Ref. Book 1: Chapter 1-4)
UNIT II KNOWLEDGE MODELS 9
Knowledge Model Components – Template Knowledge Models –Reflective Knowledge Models– Knowledge
Model Construction – Types of Knowledge Models
(Ref. Book 1: Chapter 5-7)
UNIT III TECHNIQUES OF KNOWLEDGE MANAGEMENT 9
Knowledge Elicitation Techniques – Modeling Communication Aspects – Knowledge Management and
Organizational Learning
(Ref. Book 1: Chapter 8 & 9),
UNIT IV KNOWLEDGE SYSTEM IMPLEMENTATION 9
Case Studies – Designing Knowledge Systems – Knowledge Codification – Testing and Deployment –
Knowledge Transfer and Knowledge Sharing – Knowledge System Implementation
(Ref. Book 1: Chapter 10-13 &Ref. Book 2: Chapter 7-10)
UNIT V ADVANCED KNOWLEDGE MANAGEMENT 9
Advanced Knowledge Modeling – Value Networks – Business Models for Knowledge Economy – UML
Notations – Project Management
(Ref. Book 1: Chapter 13-15& Ref. Book 5 – 61)
TOTAL : 45 PERIODS
OUTCOMES:
At the end the student will be able to
1. Apply knowledge engineering basics.
2. Design the knowledge models.
3. Apply the techniques of knowledge management and implementation.
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 34
Department of IT | REC
REFERENCES:
1. Guus Schreiber, Hans Akkermans, AnjoAnjewierden, Robert de Hoog, Nigel Shadbolt, Walter Van de
Velde and Bob Wielinga, ―Knowledge Engineering and Management‖, Universities Press, 2001
2. Elias M.Awad& Hassan M.Ghaziri, ―Knowledge Management‖, Pearson Education, 2003
3. Debowski, Shelda, "Knowledge Management: A Strategic Management Perspective",John Wiley &
Sons Ltd, 2005
4. Awad, Elias M., and Hassan M. Ghaziri. "Knowledge Management", Prentice Hall; United States
edition, 2011
5. C.W. Holsapple, ―Handbooks on Knowledge Management‖‖, International Handbooks on
Information Systems, Vol 1 and 2, 2003.
L T P C
SE17E24 PERSONAL SOFTWARE PROCESS
3 0 0 3
OBJECTIVES:
Understand the nature of PSP
Apply PSP principles in measuring software
Appreciate the role of PSP in assessing software equality
Relate PSP and TSP in software development.
Learn to use PSP in Software engineering.
UNIT I INTRODUCTION 9
Personal Process Strategy – PSP Purpose – Logic for Software Engineering Discipline – Operational
Processes – Defining and Using a Personal Process – Learning to Use a Personal Process – Baseline
Personal Process – Contents – PSP Process Elements – PSP Structure and Levels – Incremental
Development – PSP Tool Support.
UNIT II PSP SIZE ESTIMATION 9
Measuring Software Size – Size Measures – Establishing a Database Counting Standard - Establishing a LOC
Counting Standard – Size Accounting – Using Size Data – Calculating Productivity – Size Counters – Other
Size Measures – Software Estimating – Principles – Conceptual Design – Proxy Based Estimating –
Producing Relative Size Table – Estimating Considerations–Probe Estimating Method.
UNIT III PSP QUALITY MANAGEMENT 9
PSP Quality Strategy – Software Quality – Economics of Software Quality – Defect Types – Personal Quality
Practices – Quality Measures – Quality Management – Managing Product Quality – PSP Improvement
Practices – Defect Prevention.
UNIT IV PSP DESIGN TEMPLATE 9
Design Process – Design Levels – Design Strategies – Design Quality – Design Representation –Design
Templates (Operational, Functional, State and Logic) – State machine design example – Using PSP Design in
Large Scale Design – Design Verification.
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 35
Department of IT | REC
UNIT V TEAM SOFTWARE PROCESS 9
Development – Structure of TSP – Launching TSP Team – TSP Team working Process – TSP Quality
Management – TSP Status and Future Trends.
TOTAL = 45PERIODS
OUTCOMES:
At the end of this course, the students should be able to:
1. Analyze software using PSP
2. Use PSP tools to measure software quality
3. Use PSP in software design
REFERENCES:
1. Marsha Pomeroy-Huff, Robert Cannon, Timothy A. Chick, Julia Mullaney, and William Nichols,
"The Personal Software Process SM (PSP SM) Body of Knowledge, Version 2.0‖,2009
2. Watts S Humphrey, ―PSP(SM): A self-improvement process for software engineers, Addison- Wesley
Professional,2005
3. Watts S Humphrey,―Team Software Process (TSP)‖, John Wiley &Sons ,Inc., 2000.
SE17E25 SOFTWARE TEST AUTOMATION L T P C
3 0 0 3
OBJECTIVES:
Understand the basics of test automation
Appreciate the different aspects of test tool evaluation and test automation approach
selection
Understand the role played by test planning and design in test execution
Appreciate the use of various testing tools for testing varied applications
Understand test automation using case studies
UNIT I INTRODUCTION 9
Fundamentals of test automation – Management issues – technical issues - Background on software
testing – Automated test life cycle methodology (ATLM) – Test Maturity Model – Test Automation
Development – Overcoming false expectations of automated testing – benefits – test tool proposal
UNIT II TEST FRAMEWORK AND AUTOMATION 9
Test Tool Evaluation and selection – organizations- system engineering environment – tools that
support the testing life cycle – test process analysis – test tool consideration Test framework – Test
Library Management –selecting the test automation approach - test team management
UNIT III TEST PLANNING AND DESIGN 9
Test planning – Test program scope – Test requirements management – Test Events, Activities and
Documentation – Test Environment – Evolving a Test plan Test analysis and design – Test
requirements analysis – Test program design – Test procedure design – Test development
architecture – guidelines – automation infrastructure – test execution and review – test metrics.
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 36
Department of IT | REC
UNIT IV TESTING THE APPLICATIONS 9
Testing Web Applications – Functional Web testing with Twill – Selenium – Testing a simple
Web Application – Testing Mobile Smartphone Applications – Running automated test scripts –
Test tools for Browser based applications – Test Automation with Emulators.
UNIT V CASE STUDIES
9
Test automation and agile project management – database automation – test automation in
cloud – Mainframe and Framework automation – Model based test case generation – Model
based testing of Android applications – exploratory test automation.
TOTAL :45PERIODS
OUTCOMES:
At the end of this course, the students should be able to:
1. Identify the different test tools
2. Use available testing tools to test some software applications
3. Modify existing test metrics based on functionality or features used
4. Design test cases and execute them
5. Implement test scripts for automating test execution
REFERENCES:
1. C. Titus Brown, Gheorghe Gheorghiu, Jason Huggins, ―An Introduction to Testing
Web Applications with twill and Selenium ―, O'Reilly Media, Inc.,2007
2. DorothyGraha,MarkFewster,‖Experiences of Test Automation: Case Studies of
Software Test Automation‖, illustrated Edition, Addison-Wesley
Professional,2012
3. ElfriedeDustin, Jeff Rashka, ―‖‖Automated software testing: Introduction,
Management and Performance‖, Pearson Education,2008
4. Julian Harty, ―‖A Practical Guide to Testing Mobile Smartphone Applications, Vol.
6 of Synthesis Lectures on Mobile and Pervasive Computing Series‖, Morgan &
Claypool Publishers,2009
5. KanglinLi, MengqiWu, ―Effective Software Test Automation: Developing an
Automated Software Testing Tool‖,John Wiley & Sons,2006
6. Linda Hayes, ―The Automated Testing Handbook ‖, Software testing Inst., 1995
7. Mark Fewster, Dorothy Graham, ―‖‖Software Test Automation ―, AddisonWesley,1999
L T P C
SE17E26 SOFTWARE VERIFICATION AND VALIDATION
3 0 0 3
OBJECTIVES:
Understand the principles of verification and validation
Appreciate the different verification and validation techniques
Understand the various stages of testing
Appreciate the use of tools for verification and validation
Appreciate the benefits of using metrics for verification and validation
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 37
Department of IT | REC
UNIT I INTRODUCTION 9
Principles of verification and validation – software architecture frameworks – model driven architecture –
UML – systems modeling language – verification, validation and accreditation.
UNIT II METHODS OF SOFTWARE VERIFICATION 9
Verification and validation life cycle – traceability analysis – interface analysis – design and code
verification – test analysis - Reviews – inspections - walkthroughs – audits – tracing – formal proofs –
Model based verification and validation - Program verification techniques – formal methods of software
verification – clean room methods.
UNIT III TESTING 9
Stages of Testing: Test Planning – Test design – Test case definition – Test procedure – Test reporting –
Unit testing: white box , black box and performance testing – system testing: Function, performance,
interface, operations, resource, security, portability, reliability, maintainability, safety, regression and stress
testing – integration testing – acceptance testing: capability, constraint testing - structured testing –
structured integration testing.
TOOLS FOR SOFTWAREVERIFICATION
UNIT IV 9
Tools for verification and validation: static analyser – configuration management tools – reverse
engineering tools – tracing tools – tools for formal analysis – tools for testing – test case generators – test
harnesses – debuggers – coverage analysers – performance analysers – test management tools
UNIT V ADVANCEDAPPROACHES 9
Automatic approach for verification and validation – validating UML behavioral diagrams –
probabilistic model checking of activity diagrams in SysML – metrics for verification and
validation
TOTAL :45PERIODS
OUTCOMES:
At the end of this course, the students should be able to:
1. Identify the different techniques for verification and validation
2. Use available traceability analysis tools on sample requirements
3. Modify existing coverage analysers in terms of functionality or features used
4. Design system test cases
5. Use test case generators and test management tools
REFERENCES:
1. AvnerEngel, ―Verification, Validation &Testing of Engineered Systems‖, Wiley series in
systems Engineering and Management,2010.
2. ESA Board for Software Standardisationand Control(BSSC), ―Guide to software verification
and Validation, European Space Agency ESA PSS-05-10 Issue 1 Revision 1,1995
3. Marcus S. Fisher, ―Software Verification and Validation: An Engineering and
Scientific Approach, Springer, 2007
4. MouradDebbabi, Hassaine F,JarryaY.,SoeanuA.,AlawnehL.,―Verification and Validation
in Systems Engineering, Springer, 2010.
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 38
Department of IT | REC
SE17E27 SOFTWARE SECURITY L T P C
3 0 0 3
OBJECTIVES
The student should be able to
Know the importance and need of software security
Know about various attacks
Learn about secure software design
Understand risk management in secure software development
Know the working of tools related to software security
UNITI INTRODUCTION 9
Need for software security – Memory based attacks – low level attacks against heap and stack -stack
smashing – format string attacks – stale memory access attacks – ROP (Return oriented programming) –
malicious computation without code injection. Defense against memory based attacks – stack canaries –
non-executable data - address space layout randomization (ASLR), memory- safety enforcement,
control-flow Integrity (CFI) – randomization.
UNIT II SECURE DESIGN 9
Isolating the effects of untrusted executable content - stack inspection – policy specification languages –
vulnerability trends – buffer overflow – code injection - Generic network fault injection – local fault
injection - SQL injection - Session hijacking. Secure design - threat modeling and security design
principles - good and bad software design - Web security-browser security: cross-site scripting (XSS) ,
cross-site forgery (CSRF) – database security – file security.
UNIT III SECURITY RISK MANAGEMENT 9
Risk Management Life cycle – Risk Profiling – Risk exposure factors – Risk Evaluation and Mitigation
- Risk Assessment Techniques – Threat and Vulnerability Management.
UNIT IV SECURITY TESTING 9
Traditional software testing – comparison - secure software development life cycle - risk based security
testing – prioritizing security testing with threat modeling – shades of analysis: white, grey and black box
testing.
9
UNIT V ADVANCED SOFTWARE SECURITY
Advanced penetration testing – planning and scoping – DNS groper – DIG (Domain Information Graph)
– Enumeration – Remote Exploitation – Web Application Exploitation - Exploits and Client side Attacks
– Post Exploitation – Bypassing Firewalls and Avoiding Detection - Tools for penetration testing
TOTAL : 45PERIODS
OUTCOMES:
At the end the student will be able to
1. Use tools for securing software
2. Apply security principles in software development
3. Involve selection of testing techniques related to software security in testing phase of software
development
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 39
Department of IT | REC
REFERENCES:
1. BryanSullivanandVincentLiu,―WebApplicationSecurity,ABeginner'sGuide‖,KindleEdition, McGraw
Hill,2012
2. ChrisWysopal,LucasNelson,DinoDaiZovi,andElfriedeDustin,―TheArtofSoftwareSecurity Testing:
Identifying Software Security Flaws (Symantec Press)‖, Addison-Wesley Professional, 2006
3. EvanW heeler, ―Security Risk Management: Building an Information Security Risk Management
Program from the Ground Up‖, First edition, Syngress Publishing,2011
4. Lee Allen,―Advanced Penetration Testing for Highly-Secured Environments: The Ultimate Security
Guide (Open Source: Community Experience Distilled)‖, Kindle Edition, PacktPublishing,2012
5. MikeShema,―Hacking WebApps: Detecting and Preventing Web Application Security Problems, First
edition, Syngress Publishing,2012
6. RobertC.Seacord,―SecureCodinginCandC++(SEISeriesinSoftwareEngineering)‖,Addison-Wesley
Professional,2005.
CP17E36 INTERNET OF THINGS L T P C
3 0 0 3
OBJECTIVES:
The student should be made to:
To understand the fundamentals of Internet of Things
To learn about different IoT architecture
To learn about the basics of IoT protocols
To build a small low cost embedded system using Raspberry Pi.
To apply the concept of Internet of Things in the real world scenario.
UNIT I INTRODUCTION TO IoT 9
Internet of Things - Physical Design- Logical Design- IoT Enabling Technologies - IoT Levels & Deployment
Templates - Domain Specific IoTs - IoT and M2M - IoT System Management with NETCONF-YANG- IoT
Platforms Design Methodology
UNIT II IoT ARCHITECTURE 9
M2M high-level ETSI architecture - IETF architecture for IoT - OGC architecture - IoT reference model -
Domain model - information model - functional model - communication model – IoT reference architecture
UNIT III IoT PROTOCOLS 9
Web of Things versus Internet of Things - Protocol Standardization for IoT – Efforts – M2M and WSN
Protocols – SCADA and RFID Protocols – Unified Data Standards – Protocols – IEEE 802.15.4 – BACNet
Protocol – Modbus– Zigbee Architecture – Network layer – 6LowPAN - CoAP – Security
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 40
Department of IT | REC
UNIT IV BUILDING IoT WITH RASPBERRY PI & ARDUINO 9
Building IOT with RASPBERRY PI- IoT Systems - Logical Design using Python – IoT Physical Devices &
Endpoints - IoT Device -Building blocks -Raspberry Pi -Board - Linux on Raspberry Pi - Raspberry Pi
Interfaces -Programming Raspberry Pi with Python - Other IoT Platforms - Arduino
UNIT V CASE STUDIES AND REAL-WORLD APPLICATIONS 9
Real world design constraints - Applications - Asset management, Industrial automation, smart grid,
Commercial building automation, Smart cities - participatory sensing - Data Analytics for IoT – Software &
Management Tools for IoT Cloud Storage Models & Communication APIs – Cloud for IoT - Amazon Web
Services for IoT
TOTAL: 45 PERIODS
OUTCOMES:
At the end of the course, the student will be able to:
1. Analyze various protocols for IoT
2. Develop web services to access/control IoT devices.
3. Design a portable IoT using Rasperry Pi
4. Deploy an IoT application and connect to the cloud.
5. Analyze applications of IoT in real time scenario
TEXT BOOKS:
1. Olivier Hersent, David Boswarthick, Omar Elloumi , ―The Internet of Things – Key applications and
Protocols‖, Wiley, 2012
REFERENCES:
1. ArshdeepBahga, Vijay Madisetti, ―Internet of Things – A hands-on approach‖, Universities Press,
2015
2. Dieter Uckelmann, Mark Harrison, Michahelles, Florian (Eds), ―Architecting the Internet of Things‖,
Springer, 2011.
3. Honbo Zhou, ―The Internet of Things in the Cloud: A Middleware Perspective‖, CRC Press, 2012.
4. Jan Ho¨ ller, VlasiosTsiatsis, Catherine Mulligan, Stamatis,Karnouskos, Stefan Avesand. David
Boyle, "From Machine-to-Machine to the Internet of Things - Introduction to a New Age of
Intelligence", Elsevier, 2014.
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 41
Department of IT | REC
SEMESTER III
ELECTIVE- III, ELECTIVE- IV & ELECTIVE-V
SE17E31 INFORMATION RETRIVAL TECHNIQUES L T P C
3 0 0 3
OBJECTIVES:
To understand the basics of information retrieval with pertinence to modeling, query operations and
indexing
To get an understanding of machine learning techniques for text classification and clustering.
To understand the various applications of information retrieval giving emphasis to multimedia IR, web
search
To understand the concepts of digital libraries.
To know about Indexing and Searching.
UNITI INTRODUCTION MOTIVATION 9
Basic Concepts – Practical Issues - Retrieval Process – Architecture - Boolean Retrieval –Retrieval Evaluation –
Open Source IR Systems–History of Web Search – Web Characteristics–The impact of the web on IR ––IR
Versus Web Search–Components of a Search engine
UNITII MODELING 9
Taxonomy and Characterization of IR Models – Boolean Model – Vector Model - Term Weighting – Scoring and
Ranking –Language Models – Set Theoretic Models - Probabilistic Models – Algebraic Models – Structured Text
Retrieval Models – Models for Browsing
UNITIII INDEXING 9
Static and Dynamic Inverted Indices – Index Construction and Index Compression. Searching - Sequential
Searching and Pattern Matching. Query Operations -Query Languages – Query Processing - Relevance Feedback
and Query Expansion - Automatic Local and Global Analysis – Measuring Effectiveness and Efficiency
UNITIV CLASSIFICATIONAND CLUSTERING 9
Text Classification and Naïve Bayes – Vector Space Classification – Support vector machines and Machine
learning on documents. Flat Clustering – Hierarchical Clustering –Matrix decompositions and latent semantic
indexing – Fusion and Meta learning
UNITV SEARCHINGTHE WEB 9
Searching the Web –Structure of the Web –IR and web search – Static and Dynamic Ranking – Web Crawling and
Indexing – Link Analysis - XML Retrieval Multimedia IR: Models and Languages – Indexing and Searching
Parallel and Distributed IR – Digital Libraries
TOTAL : 45PERIODS
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 42
Department of IT | REC
OUTCOMES:
Upon completion of this course, the student should be able to
1. Build an Information Retrieval system using the available tools
2. Identify and design the various components of an Information Retrieval system
3. Apply machine learning techniques to text classification and clustering which is used for efficient
Information Retrieval
4. Design an efficient search engine and analyze the Web content structure.
5. Gained knowledge about Support Vector Machine.
REFERENCES:
1. Christopher D. Manning, Prabhakar Raghavan, Hinrich Schutze, ―Introduction to Information
Retrieval‖, Cambridge University Press, First South Asian Edition,2008.
2. Ricardo Baeza – Yates, Berthier Ribeiro – Neto, ―Modern Information Retrieval: The concepts and
Technology behind Search‖ (ACM Press Books), Second Edition,2011.
3. Stefan Buttcher, Charles L. A. Clarke, GordonV. Cormack, ―Information Retrieval Implementing and
Evaluating Search Engines , The MIT Press, Cambridge, Massachusetts London, England, 2010.
SE17E32 DEEP LEARNING L T P C
3 0 0 3
OBJECTIVES:
To know about the various trends involved in Deep learning.
To understand about Deep models.
To get understanding about Modeling and NLP.
To learn about Depth Stochastic Encoders.
To explore about Monte Carlo Methods.
UNIT I INTRODUCTION 9
Introduction - Historical Trends in Deep Learning -Applied Math and Machine Learning Basics -Linear Algebra-
Probability and Information Theory- Numerical Computation- Machine Learning Basics- Theoretical Advantages of
Deep Architectures- Neural Networks for Deep Architectures
UNIT II DEEP MODELS 9
Deep Networks-Modern Practices- Deep Feed forward Networks- Regularization for Deep Learning- Optimization for
Training Deep Models - Greedy Layer-Wise Training of Deep Architectures
UNIT III MODELING AND NLP 9
Modeling and Natural Language Processing -Convolutional Networks- Convolutional Networks and the History of Deep
Learning- Sequence Modeling: Recurrent and Recursive Nets- Practical Methodology- Applications
UNIT IV DEEP LEARNING RESEARCH 9
Deep Learning Research- Linear Factor Models- Auto encoders- Under complete Auto encoders- Regularized Auto
encoders- Representational Power, Layer Size and Depth Stochastic Encoders and Decoders-Denoising Auto encoders-
Learning Manifolds with Auto encoder-Contractive Auto encoders-Predictive Sparse Decomposition-Applications of
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 43
Department of IT | REC
Auto encoders
UNIT V REPRESENTATION 9
Representation Learning- Structured Probabilistic Models for Deep Learning- Monte Carlo Methods- Confronting the
Partition Function- Approximate Inference- Deep Generative Models
TOTAL: 45 PERIODS
OUTCOMES:
1. Analyzed about the various trends involved in Deep learning.
2. Gained knowledge about Deep models.
3. Have analyzed about Modeling and NLP.
4. Gathered about Depth Stochastic Encoders.
5. Learnt and applied Monte Carlo Methods.
REFERENCES
1. Ian Goodfellow, Aaron Courville and YoshuaBengio. Deep Learning. Book in preparation for MIT Press.
http://www.deeplearningbook.org/.
2. Jerome Friedman, Trevor Hastie and Robert Tibshirani. The Elements of Statistical Learning. Springer, Berlin:
Springer Series in Statistics, 2011.
3. CosmaRohillaShalizi. Advanced Data Analysis from an Elementary Point of View. Book in preparation.
http://www.stat.cmu.edu/~cshalizi/ADAfaEPoV/.
4. Stephen Boyd and LievenVandenberghe. Convex Optimization. Cambridge University Press, 2004
5. YoshuaBengio: Learning Deep Architectures for AI, Foundations & Trends in Machine Learning, 2009
SE17E33 SOFTWARE METRICS L T P C
3 0 0 3
OBJECTIVES:
Understand the fundamentals of Measurement
Know different data collection
To analyze about analysis methods
Learn software metrics that define relevant metrics in a rigorous way.
To know about the various Quality metrics.
UNIT I MEASUREMENTS THEORY 9
Fundamentals of Measurement-Measurements InSoftware Engineering-Scope of Software Metrics-Measurements
Theory-Goal Based Framework-Software Measurement Validation.
UNIT II DATA COLLECTION AND ANALYSIS 9
Empirical Investigation-Planning Experiments-Software Metrics Data Collection-Analysis Methods–Statistical Methods
UNITIII PRODUCTS METRICS 9
Measurement of Internet Product Attributes-Size And Structure-External Product Attributes-measurement of Quality.
UNITIV QUALITY METRICS 9
Software Quality- Metrics-Product Quality-Process Quality-Metrics for Software Maintenance – Case Studies of Metrics
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 44
Department of IT | REC
Program-Motorola -Hp and IBM
UNITV MANAGEMENT METRICS 9
Quality Management Models-Rayleigh Model-Problem Tracking Report (PTR) Model Reliability Growth Model-Model
Evaluation -Orthogonal Classification.
TOTAL: 45 PERIODS
OUTCOMES:
At the end the student will be able to
1. Perform some simple statistical analysis relevant to software measurement data.
2. Use from practical examples both the benefits and limitations of software metrics for quality control and
assurance.
3. Gained knowledge about software metrics that define relevant metrics in a rigorous way.
4. Gathered knowledge about various Quality metrics.
5. Analyzed the various data collection methods.
REFERENCES:
1. Norman E– Fentar, Share Lawrence P flieger, "Software Metrics", International Thomson Computer Press,
1997.
2. Stephen H. Kin, "Metric and Models in Software Quality Engineering", Addison Wesley,1995.
SE17E34 WEB DATA MINING L T P C
3 0 0 3
OBJECTIVES:
To learn about Data warehousing and Data mining
To develop about Multidimensional data Model and types
To learn about Decision Tree Induction and Classifier Evaluation
To know about a Web Mining.
To know about Web Crawling Algorithm.
UNIT I DATA WAREHOUSING AND DATA MINING 9
Definition – Components – Multidimensional Data Model – Data Cube – Dimension Modeling – OLAP
Operations – Data Warehouse Architecture Meta Data – Types of Meta Data – Data Warehouse
Implementation – Data warehouse Backend Process – Development Life Cycle – data Mining process –
Association Rules and Sequential Patterns – Apriori Algorithm – Data Formals – Mining with multiple
Minimum supports – Mining Class Association Rules – GSP – Prefix Span – Generating Rules from
Sequential Patterns.
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 45
Department of IT | REC
UNIT II CLASSIFICATION 9
Supervised Learning – Decision Tree Induction–Classifier Evaluation – Rule Induction Classification Based
on Association–Types of Classification – Unsupervised learning - K – means Clustering – Representation of
Clusters – Hierarchical Clustering-Distance Function-Data Standardization-Handling of Mixed Attributes –
Cluster Evaluation Discovering Holes and Data Regions – Partially Supervised Learning – Learning from
Labeled and unlabeled Examples-EM Algorithm – Learning from Positive and Unlabeled Examples.
UNIT III WEB MINING – RETRIEVAL, SEARCH AND LINK ANALYSIS 9
Information Retrieval- Information Retrieval Models-Relevance Feedback- Evaluation Measures – Text and
Web Page pre-Processing-Inverted Index and its Compression Latent Semantic Indexing – Web search – Meta
Search: combining Multiple Rankings Combination Using Similarity Scores – Web Spamming – Link
Analysis – Social Network Analysis Co-Citation and Bibliographic coupling-Page Rank HITS- Community
UNIT IV WEB CRAWLING AND WRAPPER GENERATION 9
Web Crawling – Algorithm – Implementation Issues – Types – Crawler Ethics and Conflicts – Structured data
Extraction: Wrapper Generation – Wrapper Induction – Instance – Based Wrapper Learning – Automatic
wrapper Generation: Problems – String Matching and Type Matching Multiple Alignment – Building DOM
Trees – Extraction Based on Multiple Pages – Using Techniques in Previous Sections- Information Integration
–Schema Matching – Pre-Processing for Schema Matching-Combining similarities-Integration of Web Query
Interfaces-Constructing a Unified Global Query Interface.
UNIT V OPINION AND WEB USAGE MINING 9
Opinion Mining –Sentiment Classification-Feature-Based Opinion Mining and Summarization –Comparative
Sentence and Relation Mining-Opinion Search-Opinion Spam –Web Usage Mining-Data Collection and Pre-
Processing-Data Modeling for Web Usage Mining-Discovery and Analysis of Web Usage Patterns -
Discussion and Outlook - Current Trends.
TOTAL: 45 PERIODS
OUTCOMES:
1. Developed to learn about Data warehousing and Data mining
2. Learnt about Multidimensional data Model and types
3. Learnt about Decision Tree Induction and Classifier Evaluation
4. Applied about a Web Mining.
5. Applied about Web Crawling Algorithm.
TEXT BOOKS:
1. Alex Berson, Stephen J. Smith ―Data Warehousing, Data Mining & OLAP‖, Tata Mcgraw- Hill, 2004.
2. Liu. B, ―Web Data Mining, Exploring Hyperlinks, Contents and Usage Data‖, Springer, 2007.
REFERENCES:
1. Reference Jiawei Han, Micheline Kamber, ―Data Mining: Concepts and Techniques‖, Morgan Kaufman
Publishers, 2000.
2. Sean Kelly, ―Data Warehousing in Action‖, John Wiley & Sons Inc., 1997.
3. Paulraj Ponnaiah, ―Data Warehousing Fundamentals‖, Wiley Publishers, 2001.
4. Usama M.Fayyad, Gregory Piatetsky Shapiro, Padhrai Smyth, Ramasamy Uthurusamy, ―Advances in
Knowledge Discover and Data Mining‖, The M.I.T Press, 1996
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 46
Department of IT | REC
SE17E35 SOFTWARE DOCUMENTATION L TP C
3 0 0 3
OBJECTIVES:
To understand task orientation and process of software documentation
To gain expertise in designing tutorials, various view types
To realize the software architecture and document interfaces
To understand document planning, conduct review and usability test
To expertise in layout pages and screens graphics usage
Analyze about software documentation.
UNIT I UNDERSTANDING TASK ORIENTATION 9
Principles of Software Documentation – Definition of Task Orientation – Theory – Forms of Software
Documentation – Procedural – Reference – Process of Software of Documentation – Seven Rules for Sound
Documentation.
UNIT II FORMS AND MODULE OF SOFTWARE DOCUMENTATION 9
Designing Tutorials – Elaborative Approach – Minimalist Approach – Writing to Guide – Procedure –
Writing to Support – Reference – Module View type - Styles of Module View Type – Component and
Connector View type – Styles of Component and Connector View type – Allocation View type and styles.
UNIT III PROCESS OF SOFTWARE DOCUMENTATION 9
Analyzing your Users – Planning and Writing your Documents – Getting Useful Reviews – Conducting
Usability Tests – Editing and Fine Tuning.
UNIT IV SOFTWARE ARCHITECTURE DOCUMENTATION IN PRACTICE 9
Chunking Information: View Packets, Refinement and Descriptive Completeness – using Context Diagrams –
Combined views – Documenting Variability and Dynamism – Documenting Software Interfaces
UNIT V TOOLS OF SOFTWARE DOCUMENTATION 9
Designing for Task Orientation – Laying out Pages and Screens – Getting the Language Right – Using
Graphics effectively – Designing Indexes.
TOTAL: 45 PERIODS
OUTCOMES:
1. Students gain knowledge of task orientation and process of software documentation
2. Students get expertise in documenting interfaces, designing layout pages, screens, graphics usage
3. Students are trained to document planning and conduct review and usability test
4. Students know-how to design tutorials, types of view etc.
5. Students are trained to handle various software tools for documentation.
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 47
Department of IT | REC
REFERENCES:
1. Paul Clements, Felix Bachman, Len Bass, David Garlan, James Ivers, Reed Little, Robert Nord, Judith
Stafford, ―Documenting Software Architectures: Views and Beyond‖, Second Edition, Addison Wesley, 2010.
2. Thomas T.Barker, ―Writing Software Documentation: A Task Oriented Approach‖, Second Edition,
Pearson Education, 2008.
CP17E37 SOCIAL NETWORK ANALYSIS L T P C
3 0 0 3
OBJECTIVES:
The student should be made to:
To understand the components of the social network.
To model and visualize the social network.
To mine the users in the social network.
To understand the evolution of the social network.
To know the applications in real time systems.
UNIT I INTRODUCTION 9
Introduction to Semantic Web - Limitations of current Web – Development of Semantic Web – Emergence of
the Social Web – Social Network analysis - Development of Social Network Analysis - Key concepts and
measures in network analysis - Electronic sources for network analysis - Blogs and online communities -
Web-based networks.
UNIT II MODELING AND VISUALIZATION 9
Statistical Properties of Social Networks - Static &Dynamic Properties - Random Walks on Graphs –
Background & Applications - Evaluation and datasets Visualizing Social Networks - A Taxonomy of
Visualizations - The Convergence of Visualization- Interaction and Analytics.
UNIT III COMMUNITY, NODE AND DATA MINING 9
Communities in Context - Core Methods - Emerging Fields and Problems - Community Discovery in different
networks - Node Classification- Problem Formulation - Methods using Local Classifiers - Random Walk
based Method - Applying Node Classification to Large Social Networks - Data Mining- Introduction - Data
Mining Methods - Related Efforts
UNIT IV EVOLUTION 9
Evolution in Social Networks – Framework - Challenges of Social Network Streams - Tracing Smoothly
Evolving Communities - Laws of Evolution in Social Networks - Models and Algorithms for Social Influence
Analysis- Influence Related Statistics – Social Similarity and Influence - Influence Maximization in Viral
Marketing - Algorithms and Systems for Expert Location in Social Networks- Expert Location without Graph
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 48
Department of IT | REC
Constraints - with Score Propagation – Expert Team Formation - Link Prediction in Social Networks: Feature
based Link Prediction – Bayesian Probabilistic Models - Probabilistic Relational Models.
UNIT V APPLICATIONS 9
A Learning Based Approach for Real Time Emotion Classification of Tweets - A New Linguistic Approach to
Assess the Opinion of Users in Social Network Environments- Visual Analysis of Topical Evolution in
Unstructured Text: Design and Evaluation of Topic Flow - Explaining Scientific and Technical Emergence
Forecasting - Social Network Analysis for Biometric Template Protection
TOTAL: 45 PERIODS
OUTCOMES:
At the end of the course, the student will be able to:
1. Work on the internals components of the social network
2. Model and visualize the social network
3. Mine the behavior of the users in the social network
4. Predict the possible next outcome of the social network
5. Apply social network in real time applications
TEXT BOOKS:
1. Peter Mika, ―Social Networks and the Semantic Web‖, Springer, 1st edition, 2007.
2. Przemyslaw Kazienko, Nitesh Chawla, ―Applications of Social Media and Social Network Analysis‖,
Springer,2015
REFERENCES:
1. Ajith Abraham, Aboul Ella Hassanien, VáclavSnasel, ―Computational Social Network Analysis:
Trends, Tools and Research Advances‖, Springer, 2012
2. BorkoFurht, ―Handbook of Social Network Technologies and Applications‖, Springer, 1st edition,
2011
3. Charu C. Aggarwal, ‖Social Network Data Analytics‖, Springer; 2014
4. Giles, Mark Smith, John Yen, ―Advances in Social Network Mining and Analysis‖, Springer, 2010.
5. GuandongXu , Yanchun Zhang and Lin Li, ‖Web Mining and Social Networking Techniques and
applications‖, Springer, 1st edition, 2012
SE17E36 SOFTWARE RELIABILITY L T P C
3 0 0 3
OBJECTIVES:
To know about Software reliability.
To learn about Software reliability modelling.
To analyze the comparison of software reliability models.
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 49
Department of IT | REC
Learnt about architecture and design techniques.
To understand about the software reliability models.
UNIT I INTRODUCTION TO SOFTWARE RELIABILITY 7
Basic Concepts – Failure and Faults – Environment – Availability –Modeling –uses.
UNIT II SOFTWARE RELIABILITY MODELING 12
Concepts – General Model Characteristic – Historical Development of models – Model Classification scheme
– Markovian models – General concepts – General Poisson Type Models – Binomial Type Models – Poisson
Type models – Fault reduction factor for Poisson Type models.
UNIT III COMPARISON OF SOFTWARE RELIABILITY MODELS 10
Comparison Criteria – Failure Data – Comparison of Predictive Validity of Model Groups – Recommended
Models – Comparison of Time Domains – Calendar Time Modeling – Limiting Resource Concept – Resource
Usage model – Resource Utilization – Calendar Time Estimation and confidence Intervals.
UNIT IV ARCHITECTURE AND DESIGN TECHNIQUES 8
Fault Tolerance Techniques- Error Handling for Safe Systems – Environment Independence Techniques-
Development Techniques- Components- Minimization of Faults – Fault Avoidance Techniques – Key Design
Principles- Rejuvenation Techniques.
UNIT V MEASUREMENT AND TOOLS 8
Software Reliability Measurement Experience- Measurement Based Analysis of Software Reliability- Tools –
CASRE – SoftRel.
TOTAL: 45 PERIODS
OUTCOMES:
1. Analyzed about Software reliability.
2. Gained knowledge about Software reliability modeling.
3. Developed comparison of software reliability models.
4. Applied architecture and design techniques in realistic environment.
5. Studied about the software reliability models.
REFERENCES:
1. John D. Musa, Anthony Iannino, KazuhiraOkumoto, ―Software Reliability – Measurement, Prediction,
Application, Series in Software Engineering andTechnology‖, McGraw Hill, 1987.
2. John D. Musa, ―Software Reliability Engineering‖, Tata McGraw Hill, 1999.
3. Jalote,―Fault Tolerance in Distributed Systems‖, Prentice Hall, 1998.
4. Michael R. Lyu, ―Handbook of Software Reliability Engineering‖, McGraw Hill, 1996.
Curriculum and Syllabus | M.E Software Engineering | R2017 Page 50
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