JNTU Kakinada (JNTUK) M.Tech R20-R19-R18 Syllabus And Course Structure
DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING
COURSE STRUCTURE & SYLLABUS M.Tech CSE for
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SOFTWARE ENGINEERING PROGRAMME
(Applicable for batches admitted from 2019-2020)
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY KAKINADA
I-SEMESTER
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| S.N | Course Code | Courses | Category | L | T | P | C |
|---|---|---|---|---|---|---|---|
| S.No | Course Code | Courses | Category | L | T | P | C |
| 1 | MTSE1101 | Program Core-1 Software Engineering | PC | 3 | 0 | 0 | 3 |
| 2 | MTSE1102 | Program Core-2 Advanced Data Structures | PC | 3 | 0 | 0 | 3 |
| 3 | MTSE1103 | Program Elective-1 1. Software Project and Process Management 2. Machine Learning 3. E-Commerce | PE | 3 | 0 | 0 | 3 |
| 4 | MTSE1104 | Program Elective-2 1. Software Quality Assurance and Testing 2. Cloud Computing 3. Internet of Things | PE | 3 | 0 | 0 | 3 |
| 5 | MTSE1105 | Research Methodology and IPR | CC | 0 | 2 | ||
| 6 | MTSE1106 | Laboratory-1 Advanced Data Structures Lab | LB | 0 | 0 | 4 | 2 |
| 7 | MTSE1107 | Laboratory-2 SE LAB-I | LB | 0 | 0 | 4 | 2 |
| 8 | MTSE1108 | Audit Course-1* | AC | 2 | 0 | 0 | 0 |
| Total Credits | 18 |
*Student has to choose any one audit course listed below.
II-SEMESTER
| S.N | Course Code | Courses | Category | L | T | P | C |
|---|---|---|---|---|---|---|---|
| 1 | MTSE1201 | Program Core-3 Service Oriented Architecture | PC | 3 | 0 | 0 | 3 |
| 2 | MTSE1202 | Program Core-4 Mathematical Foundations of Computer Science | PC | 3 | 0 | 0 | 3 |
| 3 | MTSE1203 | Program Elective-3 1. Software Testing Methodologies 2. Agile Software Development 3. ERP & Supply Chain Management | PE | 3 | 0 | 0 | 3 |
| 4 | MTSE1204 | Program Elective-4 1. Secure Software Engineering 2. Big Data Analytics 3. Design patterns | PE | 3 | 0 | 0 | 3 |
| 5 | MTSE1205 | Laboratory-3 Software Testing Lab | LB | 0 | 0 | 4 | 2 |
| 6 | MTSE1206 | Laboratory-4 SE LAB-II | LB | 0 | 0 | 4 | 2 |
| 7 | MTSE1207 | Mini Project with Seminar | MP | 2 | 0 | 0 | 2 |
| 8 | MTSE1208 | Audit Course-2 * | AC | 2 | 0 | 0 | 0 |
| Total Credits | 18 |
III-SEMESTER
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| S.N | Course Code | Courses | Category | L | T | P | C |
|---|---|---|---|---|---|---|---|
| 1 | MTSE2101 | Program Elective-5 1. Object Oriented Software Engineering 2. Artificial Intelligence 3. User Interface Design 4. MOOCS-I(NPTEL/SWAYAM- 12 Week Program related to the programme which is not listed in the course structure | PE | 3 | 0 | 0 | 3 |
| 2 | MTSE2102 | Open Elective 1. MOOCS-II (NPTEL/SWAYAM- Any 12 Weeks Program-Interdisciplinary Course but not from Parent Department) 2. Courses offered by other departments in the college | OE | 3 | 0 | 0 | 3 |
| 3 | MTSE2103 | Dissertation-I/ Industrial Project# | PJ | 0 | 0 | 20 | 10 |
| Total Credits | 16 |
*Student has to choose any one audit course listed below.
Audit Course 1 & 2:
1. English for Research Paper Writing
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2. Disaster Management
3. Sanskrit for Technical Knowledge
4. Value Education
5. Constitution of India
6. Pedagogy Studies
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7. Stress Management by Yoga
8. Personality Development through Life Enlightenment Skills
#Students going for Industrial Project/Thesis will complete these courses through MOOCS
IV-SEMESTER
| S.No | Course Code | Courses | Category | L | T | P | C |
|---|---|---|---|---|---|---|---|
| 1 | MTSE2201 | Dissertation-II | PJ | 0 | 0 | 32 | 16 |
| Total Credits | 16 |
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Open Electives offered to Other Departments
1. Python Programming
2. Artificial Intelligence
3.Machine Learning
4.Deep Learning
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I Year - I Semester
| L | T | P | C |
|---|---|---|---|
| 3 | 0 | 0 | 3 |
Software Engineering (MTSE1101)
Course Objectives:
In this course the student will be learn about
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• The role of software, aim of the software system, different types of process models.
• How to use process models in project, software requirement specification, Requirement and analysis,planningof a software project, estimations, Riskmanagement.
• Role of software architecture, architecture views and Architecture styles for C&C view, evaluating architectures.
• Design concepts, function-oriented design, object oriented design, and metrics.
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• Developing code for system, different types of testings' applying on developed system.
Course Outcomes:
By the end of course the student will be able
• Demonstrate knowledge on:
? Fundamental concepts of software engineering.
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? Process models.
? Software development life cycle.
• Analyze software requirements and process models required to develop a software system.
• Design and develop a quality software product using design engineeringprinciples and Develop software product as per user and societal requirements.
• Follow standards for software development and quality management.
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• Demonstrate skills in applying risk and quality management principles for effective management of software projects.
UNIT-I: Software and Software Engineering: The Nature of Software, The Unique Nature of WebApps, Software Engineering, Software Process, Software Engineering Practice, Software Myths. Process Models: A Generic Process Model, Process Assessment and Improvement, Prescriptive Process Models, Specialized Process Models, The Unified Process, Personal and Team Process Models, Process Terminology, Product and Process.
UNIT-II: Requirements Analysis And Specification: Requirements Gathering and Analysis, Software Requirement Specification (SRS), Formal System Specification.
Software Design: Overview of the Design Process, How to Characterise of a Design? Cohesion and Coupling, Layered Arrangement of Modules, Approaches to Software Design
UNIT III: Function-Oriented Software Design: Overview of SA/SD Methodology, Structured Analysis, Developing the DFD Model of a System, Structured Design, Detailed Design, Design Review, over view of Object Oriented design. User Interface Design: Characteristics of Good User Interface, Basic Concepts, Types of User Interfaces, Fundamentals of Component-based GUI Development, A User Interface Design Methodology.
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UNIT IV:Coding And Testing: Coding, Code Review, Software Documentation, Testing, Unit Testing, Black-Box Testing, White-Box Testing, Debugging, Program Analysis Tool, Integration Testing, Testing Object-Oriented Programs, System Testing, Some General Issues Associated with Testing
UNIT - V:Software Reliability And Quality Management: Software Reliability, Statistical Testing, Software Quality, Software Quality Management System, ISO 9000, SEI Capability Maturity Model. Computer Aided Software Engineering: Case and its Scope, Case Environment, Case Support in Software Life Cycle, Other Characteristics of Case Tools, Towards Second Generation CASE Tool, Architecture of a Case Environment
Text Books:
1. Software Engineering A practitioner's Approach, Roger S. Pressman, Seventh Edition McGraw Hill International Edition.
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2. Fundamentals of Software Engineering, Rajib Mall, Third Edition, PHI.
3. Software Engineering, Ian Sommerville, Ninth edition, Pearson education
Reference Books:
1. Software Engineering : A Primer, Waman S Jawadekar, Tata McGraw-Hill, 2008
2. Software Engineering, A Precise Approach, PankajJalote, Wiley India, 2010.
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3. Software Engineering, Principles and Practices, Deepak Jain, Oxford University Press.
4. Software Engineering1: Abstraction and modeling, Diner Bjorner, Springer International edition, 2006.
I Year - I Semester
| L | T | P | C |
|---|---|---|---|
| 3 | 0 | 0 | 3 |
Advanced Data Structures (MTSE1102)
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Course Objectives:
From the course the student will learn
• Single Linked, Double Linked Lists, Stacks, Queues, Searching and Sorting techniques, Trees, Binary trees, representation, traversal, Graphs- storage, traversal.
• Dictionaries, ADT for List, Stack, Queue, Hash table representation, Hash functions, Priority queues, Priority queues using heaps, Search trees.
• AVL trees, operations of AVL trees, Red- Black trees, Splay trees, comparison of search trees.
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Course Outcomes:
• Ability to write and analyze algorithms for algorithm correctness and efficiency.
• Master a variety of advanced abstract data type (ADT) and data structures and their Implementation.
• Demonstrate various searching, sorting and hash techniques and be able to apply and solve problems of real life.
• Design and implement variety of data structures including linked lists, binary trees, heaps, graphs and search trees.
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• Ability to compare various trees and find solutions for IT related problems.
UNIT-I: Introduction to Data Structures- Singly Linked Lists, Doubly Linked Lists, Circular Lists-Algorithms, Stacks and Queues- Algorithm Implementation using Linked Lists.
UNIT-II: Searching- Linear and Binary, Search Methods, Sorting- Bubble Sort, Selection Sort, Insertion Sort, Quick Sort, Merge Sort, Trees- Binary trees, Operations- Insertion, Deletion, Properties, Representation and Traversals (DFT, BFT), Expression Trees (Infix, prefix, postfix), Graphs- Basic Concepts, Storage structures and Traversals.
UNIT-III: Dictionaries, ADT, The List ADT, Stack ADT, Queue ADT, Hash Table Representation, Hash Functions, Collision Resolution-Separate Chaining, Open Addressing- Linear Probing, Double Hashing.
UNIT-IV: Priority queues- Definition, ADT, Realising a Priority Queue Using Heaps, Definition, Insertion, Deletion, Search Trees- Binary Search Trees, Definition, ADT, Implementation, Operations- Searching, Insertion, Deletion.
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UNIT-V: Search Trees- AVL Trees, Definition, Height of AVL Tree, Operations- Insertion, Deletion and Searching. Introduction to Red-Black and Splay Trees, B- Trees, Height of B-Tree, Insertion, Deletion and Searching, Comparison of Search Trees.
Text Books:
1. Data Structures: A Pseudocode Approach, 2/e, Richard F.Gilberg, Behrouz A. Forouzon, Cengage
2. Data Structures, Algorithms and Applications in java, 2/e, SartajSahni, University Press
Reference Books:
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1. Data Structures And Algorithm Analysis, 2/e, Mark Allen Weiss, Pearson
2. Data Structures And Algorithms, 3/e, Adam Drozdek, Cenage
3. C and Data Structures: A Snap Shot Oriented Treatise Using Live Engineering Examples, N. B. Venkateswarulu, E.V. Prasad, S Chand & Co, 2009
4. Classic Data Structures, Second Edition, Debasis Samantha,PHI
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I Year - I Semester
| L | T | P | C |
|---|---|---|---|
| 3 | 0 | 0 | 3 |
Software Project and Process Management (MTSE11XX)
Course Objectives:
At the end of the course, the student shall be able to:
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• To describe and determine the purpose and importance of project management from the perspectives of planning, tracking and completion of project.
• To compare and differentiate organization structures and project structures.
• To implement a project to manage project schedule, expenses and resources with the application of suitable project management tools.
Course outcomes:
Upon the completion of the course students will be able to:-
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• Apply the process to be followed in the software development life-cycle models.
• Implement communication, modeling, and construction & deployment practices in software development.
• Analyze & design the software models using unified modeling language (UML) and the concepts of various software testing methods.
• Apply appropriate testing approaches for development of software and use the quality management metrics in software development.
• Apply the concepts of project management & planning.
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UNIT-I : Software Process Maturity Software maturity Framework, Principles of Software Process Change, Software Process Assessment, The Initial Process, The Repeatable Process, The Defined Process, The Managed Process, The Optimizing Process. Process Reference Models Capability Maturity Model (CMM), CMMi, PCMM, PSP, TSP.
UNIT-II: Software Project Management Renaissance Conventional Software Management, Evolution of Software Economics, Improving Software Economics, The old way and the new way.
UNIT-III: Life-Cycle Phases and Process artifacts Engineering and Production stages, inception phase, elaboration phase, construction phase, transition phase, artifact sets, management artifacts, engineering artifacts and pragmatic artifacts, model based software architectures. Workflows and Checkpoints of process Software process workflows, Iteration workflows, Major milestones, minor milestones, periodic status assessments.
UNIT-IV: Process Planning and Project Organizations Work breakdown structures, Planning guidelines, cost and schedule estimating process, iteration planning process, Pragmatic planning, line-of- business organizations, project organizations, evolution of organizations, process automation.
UNIT-V: Project Control and process instrumentation The seven core metrics, management indicators, quality indicators, life-cycle expectations, Pragmatic software metrics, metrics automation. CCPDS-R Case Study and Future Software Project Management Practices Modern Project Profiles, Next-Generation software Economics, Modern Process Transitions
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Text Books:
1. Managing the Software Process, Watts S. Humphrey, Pearson Education, 1999
2. Software Project Management, Walker Royce, Pearson Education, 1998
Reference Books:
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1. An Introduction to the Team Software Process, Watts S. Humphrey, Pearson Education, 2000
2. Process Improvement essentials, James R. Persse, O'Reilly,2006
3. Software Project Management, Bob Hughes & Mike Cotterell, fourth edition, Tata Mc-Graw Hill,2006
4. Applied Software Project Management, Andrew Stellman & Jennifer Greene, O'Reilly, 2006.
5. Head First PMP, Jennifer Greene & Andrew Stellman, O'Reilly,2007
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I Year - I Semester
| L | T | P | C |
|---|---|---|---|
| 3 | 0 | 0 | 3 |
Machine Learning (MTSE11XX)
Course Objectives:
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• Identify problems that are amenable to solution by AI methods, and which AI methods may be suited to solving a given problem.
• Formalize a given problem in the language/framework of different AI methods (e.g., as a search problem, as a constraint satisfaction problem, as a planning problem, as a Markov decision process, etc).
• Implement basic AI algorithms (e.g., standard search algorithms or dynamic programming).
• Design and carry out an empirical evaluation of different algorithms on problem formalization, and state the conclusions that the evaluation supports.
Course Outcomes:
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After the completion of the course, student will be able to
• Explain the definition and usage of the term 'the internet of things' in different contexts.
• Demonstrate on various network protocols used in IoT.
• Analyze on various key wireless technologies used in IoT systems, such as WiFi, 6LoWPAN, Bluetooth and ZigBee.
• Illustrate on the role of big data, cloud computing and data analytics in IoT system.
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• Design a simple IoT system made up of sensors, wireless network connection, data analytics and display/actuators, and write the necessary control software.
Unit-I: Introduction-Towards Intelligent Machines, Well posed Problems, Example of Applications in diverse fields, Data Representation, Domain Knowledge for Productive use of Machine Learning, Diversity of Data: Structured / Unstructured, Forms of Learning, Machine Learning and Data Mining, Basic Linear Algebra in Machine Learning Techniques.
Unit-II: Supervised Learning- Rationale and Basics: Learning from Observations, Bias and Why Learning Works: Computational Learning Theory, Occam's Razor Principle and Overfitting Avoidance Heuristic Search in inductive Learning, Estimating Generalization Errors, Metrics for assessing regression, Metrics for assessing classification.
Unit-III: Statistical Learning- Machine Learning and Inferential Statistical Analysis, Descriptive Statistics in learning techniques, Bayesian Reasoning: A probabilistic approach to inference, K-Nearest Neighbor Classifier. Discriminant functions and regression functions, Linear Regression with Least Square Error Criterion, Logistic Regression for Classification Tasks, Fisher's Linear Discriminant and Thresholding for Classification, Minimum Description Length Principle.
Unit-IV: Support Vector Machines (SVM)- Introduction, Linear Discriminant Functions for Binary Classification, Perceptron Algorithm, Large Margin Classifier for linearly seperable data, Linear Soft Margin Classifier for Overlapping Classes, Kernel Induced Feature Spaces, Nonlinear Classifier, Regression by Support vector Machines.
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Learning with Neural Networks: Towards Cognitive Machine, Neuron Models, Network Architectures, Perceptrons, Linear neuron and the Widrow-Hoff Learning Rule, The error correction delta rule
Unit -V: Multilayer Perceptron Networks and error back propagation algorithm, Radial Basis Functions Networks. Decision Tree Learning: Introduction, Example of classification decision tree, measures of impurity for evaluating splits in decision trees, ID3, C4.5, and CART decision trees, pruning the tree, strengths and weakness of decision tree approach.
Textbooks:
1. Applied Machine Learning, M.Gopal, McGraw Hill Education
2. Machine Learning: A Probabilistic Perspective, Kevin Murphy, MIT Press,2012
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3. The Elements of Statistical Learning, Trevor Hastie, Robert Tibshirani, Jerome Friedman, Springer 2009 (freely available online)
Reference Books:
1. Pattern Recognition and Machine Learning, Christopher Bishop, Springer,2007
2. Programming Collective Intelligence: Building Smart Web 2.0 Applications - Toby Segaran
3. Building Machine Learning Systems with Python - WilliRichert, Luis Pedro Coelho
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I Year - I Semester
| L | T | P | C |
|---|---|---|---|
| 3 | 0 | 0 | 3 |
E-Commerce (MTSE11XX)
Course Objectives:
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This course provides
• An introduction to information systems for business and management.
• It is designed to familiarize students with organizational and managerial foundations of systems.
• Technical foundation for understanding information systems.
Course Outcomes:
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• Demonstrate an understanding of the foundations and importance of E- commerce
• Analyze the impact of E-commerce on business models and strategyDiscuss legal issues and privacy in E-Commerce
• Describe Internet trading relationships including Business to Consumer, Business-to-Business, Intra-organizational.
• Describe the infrastructure for E-commerce and describe the key features of Internet, Intranets and Extranets and explain how they relate to each other.
• Assess electronic payment systems and Recognize and discuss global E- commerce issues
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UNIT-I: Electronic Commerce, Frame work, anatomy of E-Commerce applications, E- Commerce Consumer applications, E-Commerce organization applications. Consumer Oriented Electronic commerce, Mercantile Process models.
UNIT-II: Electronic payment system-Digital Token Based, SmartCards, CreditCards, Risks in Electronic Payment systems.
UNIT-III: Inter Organizational Commerce-EDI, EDI Implementation, Value added networks. Intra Organizational Commerce-work Flow, Automation Customization and internal Commerce, Supply chain Management.
UNIT-IV: Corporate Digital Library - Document Library, digital Document types, corporate Data Warehouses. Advertising and Marketing, Information based marketing, Advertising on Internet, on-line marketing process, market research.
UNIT-V: Consumer Search and Resource Discovery, Information search and Retrieval, Commerce Catalogues, Information Filtering. Multimedia –key multimedia concepts, Digital Video and electronic Commerce, Desktop video processing's, Desktop video conferencing.
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Text Book:
1. Frontiers of ElectronicCommerce, Kalakata, Whinston, PEA,2006.
Reference Books:
1. E-Commerce Fundamentals and Applications, Hendry Chan, Raymond Lee, Dillon, Chang, John Wiley.
2. E-Commerce, A Managerial Perspective, Turban E, LeeJ, King, ChungH.M.,PEA, 2001.
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3. E-Commerce An Indian Perspective, Joseph, PHI, 2009.
4. E-Commerce, S.Jaiswal, Galgotia.
5. Electronic Commerce, Gary P.Schneider, Thomson.
I Year - I Semester
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| L | T | P | C |
|---|---|---|---|
| 3 | 0 | 0 | 3 |
Software Quality Assurance and Testing (MTSE11XX)
Course Objectives:
The student should be able to:
• Demonstration of software quality assurance and testing as a fundamental component of software lifecycle.
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• Define the scope of software projects.
• Apply software quality assurance and testing activities using modern software tools.
• Estimate cost of a project and manage budgets and prepare test plans and schedules for a software quality assurance and testing project.
• Develop software quality assurance and testing project staffing requirements and effectively manage a project.
Course Outcomes:
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• Apply modern software testing processes in relation to software development and project management.
• Create test strategies and plans, design test cases, prioritize and execute them.
• Manage incidents and risks within a project.
• Contribute to efficient delivery of software solutions and implement improvements in the software development processes.
• Gain expertise in designing, implementation and development of computer based systems and IT processes.
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UNIT-I: Software quality assurance Framework and Standards SQA Frame work: What is Quality? Software Quality Assurance. Components of Software quality Assurance.
Software Quality Assurance Plan: Steps to develop and implement a Software quality Assurance Plan. Standards: ISO9000, CMMI, CMM, PCMM, Malcom Balridge, 3 Sigma, 6 Sigma .
UNIT-II: Software Quality Assurance Metrics and Measurement Software Quality Assurance Metrics: Product Quality metrics, In- Process Quality metrics, Metrics for Software Maintenance. Examples of Metric Programs, Software quality indicators Fundamentals in Measurement Theory
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