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Admissions to MCA & MSc. Programmes is through the common entrance test conducted by the Computer Science Department, University of Pune.

Admissions to MTech Programme is through GATE Score and Advertisement.

Degree Programmes

  • MCA - 3 years
    The MCA degree primarily aims at training for professional practice in the industry. The programme is designed so that the graduate can adapt to any specific need with ease. The duration of the study is six semesters, which is normally completed in three years. Selection is through the Qualifying Exam and satisfying the eligibility criteria.

  • MSc - 2 years
    The MSc degree prepares the student for higher studies in Computer Science. The duration of the study is four semesters, which is normally completed in two years. An year long project provides an opportunity to apply the principles to a significant problem. Selection is through the Qualifying Exam and satisfying the eligibility criteria.

  • MTech - 2 years
    The MTech degree is a first level degree in Computer Science for graduates in any engineering discipline except Computer Science. This programme also primarily aims at training for professional practice in the industry. The programme is designed so that the graduate can adapt to any specific need with ease. The duration of the study is four semesters, which is normally completed in two years. An year long project provides an opportunity to apply the principles to a significant problem. Selection is through the Qualifying Exam and satisfying the eligibility criteria.

  • Eligibility :
    GATE score in Engineering or any Mathematical or Physical Sciences or UGC/CSIR JRF qualification, valid in July of year of entrance exam.

NOTE:

  • For information concerning GATE, contact the GATE office at any Indian Institute of Technology.

  • Candidates qualifying GATE in Computer Science: please note that our M.Tech. programme is a first-level programme in Computer Science.

  • Foreign nationals studying in Indian Universities will be judged by the same criteria as those applied to Indian nationals. In particular, they have to appear for the Entrance Exam. Additional Requirements for Reserved Categories

  • Candidates belonging to the following categories are required to submit the following documents at the time of admission.

    Physically handicapped:
    A medical certificate from a registered physician. The handicapped status will be verified by a physician approved by the University of Pune.

    Kashmir Quota:
    Letter from Directorate of Higher and Technical Education, Government of Maharashtra.

    SC/ST:
    Attested copy of caste certicate.

    DT/NT/OBC:
    Attested photocopy of caste certificate issued by Govt. of Maharashtra, and creamy layer free certificate if applicant claims reservation under NT(C), NT(D) and OBC.

If selected candidates cannot submit these documents, their admission will be cancelled. Candidates of reserved categories recognised by states other than Maharashtra will not be considered for these reserved seats.

Common Courses (First two semesters)

Courses Specific to M. Sc. (Last two semesters)

Courses Specific to M.Tech. (Last two semesters)

Elective Courses (offered in the last few years)

  • Advanced Computer Architecture

  • Advanced Theoretical

  • Computer Science

  • Advanced Topics in DBMS

  • Artificial Intelligence and Tools

  • Category Theory

  • Code Optimization

  • Compiler Construction

  • Data Warehousing and Mining

  • Discrete Optimization

  • Implementation of RDBMS

  • Geometric Modelling

  • Issues in Programming

  • Logic Programming

  • Parallel Algorithms

  • Parallel Architectures

  • Programming Languages:Theory and Implementation

  • Soft Computing

  • Software Tools

  • Spatial Information Systems

  • System Management and Modeling

  • User Interface Design


CS-101 - Introduction to Programming

  • Aims and Objectives
    To give students the grounding that makes it possible to approach problems and solve them on the computer.

  • The aspects covered range across:

  1. Modelling a given problem domain appropriately

  2. Designing a solution

  3. Implementing the solution in a high level programming language

  • Contents
    Two paradigms are used as vehicles to carry the ideas and execute practicals for this course the functional and the imperative.

    The Functional Paradigm :
    The central issue here is to be able to use the computer as a highlevel tool for problem solving. The paradigm conveyed may be simply expressed as:

    A modern nonstrict functional language with a polymorphic type system is the medium for this part. The currently used language is the internationally standardized language, Haskell.

    Important ideas that are to be covered include:

  1. Standard Constructs
    Function and type definition, block structure.
    Guarded equations, pattern matching.
    Special syntax for lists, comprehension.

  2. Standard Data Types Fluency is to be achieved in the standard data types: numbers, boolean, character, tuple, list.
    List programs in an algebraic vein.
    Lists in the context of general collections sets, bags, lists, tuples. (MF)

  3. calculus
    A direct way for denoting functions.

  4. First Classness
    All values are uniformly treated and conceptualized.

  5. Higher Order Functions Use of first class, higher order functions to capture large classes of computations in a simple way. An understanding of the benefits that accrue modularity, flexibility, brevity, elegance.

  6. Laziness The use of infinite data structures to separate control from action.

  7. Type discipline

  8. Polymorphism:
    The use of generic types to model and capture large classes of data�structures by factorizing common patterns.

  9. Inference
    The types of expressions may be determined by simple examination of the program text.
    Understanding such rules.

  10. User defined types
    User defined types as
    a means to model
    a means to extend the language
    a means to understand the built in types in a uniform framework.

  11. Concrete types
    Types are concrete. i.e. values that are read or written by the system correspond directly to the abstractions that they represent. More specifically, unlike abstract types which are defined in terms of admissable operations, concrete types are defined by directly specifying the set of possible values.

  12. Recursion
    Recursive definitions as
    a means of looping indefinitely
    a structural counterpart to recursive data type definitions
    a means to understand induction in a more general framework than just for natural numbers

  13. Operational Semantics
    Functional programs execute by rewriting.
    calculus as a rewriting system
    Reduction, confluence, reasons for preferring normal order reduction.

  14. Type Classes
    Values are to types as types are to classes. Only elementary ideas.

  • The Imperative Paradigm :
    The imperative paradigm is smoothly introduced as follows:

Worlds The Timeless World World of Time
Domain Mathematics Programming
Syntax Expressions Statements
Semantics Values Objects
Explicit Data Structures Control Structure
Think with Input Output relations State Change
Abstractions Functions Procedures
Relation Denote programs Implement functions

In the following we spell out some of the points of how FP translates into Imp P. The examples may be analogized from say how one would teach assembly language to someone who understands structured programming.

  1. Semantic relations The central relation is that imperative programming's denotational semantics is FP, FP's operational semantics is imperative programming.

  2. Operational Thinking
    IN FP data dependency implicitly determines sequencing whereas in Imp P it is done explicitly. Advantages and disadvantages of operational thinking.

  3. Environment
    In imperative programming there is a single implicit environment memory. In FP there are multiple environments; which could be explicit to the point of first classness (the value of variables bound in environments could be other environments). Use of environments to model data abstraction, various object frameworks, module systems.

  4. Semi Explicit Continuation
    Explicit in the sense that goto labels can be dealt with firstclassly (as in assembly), but not explicit in the sense of capturing the entire future of a computation dynamic execution of a code block may be 'concave'.

  5. Recursion iteration equivalence
    General principles as well as scheme semantics of tailrecursion.

  6. Type Issues
    Monomorphic, polymorphic and latent typing: translating one into another.

  7. Guile
    A variety of vehicles have been used for the imperative paradigm, eg. Pascal, C, Java,Tcl. The current choice is Scheme in the guile dialect because it gives a full support for the functional and the imperative paradigm. In fact Guile has been chosen over C because the single data structure in guile sexpressions is universal (aka XML) and thus imperative and functional thinking do not quarrel with datastructure issues.

    Orthogonal kinds of abstractions, which are usually considered 'advanced', such as functional, higherorder functional, objectoriented, streambased, datadriven, language extensions via eval, via macros, via C can be easily demonstrated. In fact, once guile has been learnt, it is much faster to pick up C in the subsequent semester.

    Note: In addition to being a system programming and general purpose language Guile is also a scripting, extension and database programming language because it is the flagship language for FSF (The free software foundation).

  • Bibliography
    Introduction to Functional Programming, Bird and Wadler, Prentice Hall
    Algebra of Programs, Bird, Prentice Hall
    Structure and Interpretation of Computer Programs, Abelson and Sussman, MIT Press
    Scheme and the Art of Programming, Friedmann and Haynes, MIT Press
    Equations Models and Programs,, Thomas Myers, Prentice Hall
    Algorithms + Data Structures = Programs, N Wirth
    Functional Programming, Reade
    Programming from First Principles, Bornat, Prentice Hall
    Discrete Maths with a computer, Hall and Donnell, Springer Verlag
    Guile Reference Manual, www.gnu.org

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CS-102 Logical Organization of Computers
  • Aims
    There are two views of computer architecture. The traditional view, dating back to the IBM System/360 from the early 1960's, is that the architecture of a computer is the programmer-visible view of the machine, while its implementation is the province of the hardware designer. Nowadays this is known as instruction set architecture. In the more recent view, computer architecture has three parts: instruction set architecture, computer organization and hardware. Here computer organization refers to the high-level, abstract or essential aspects of a machine's structure and behavior. The CO course is intended to give the basic architecture background that software professionals need. In that sense it is a basic, first level course meant to provide the prerequisite for further study. However there is also an open, research oriented side to it. In the earliest days of computing the programmer and the hardware engineer were the same person but across the next 50 years the two fields have separated so rigidly that they can hardly understand each other today. Recent trends are bringing these two trends back together. So a second(ary) aim of this course is make it a preliminary towards appreciating and participating in these trends.

  • Contents

  1. From a calculator to a stored-program computer
    The functionality of a calculator: electronics to perform arithmetic operations; memory to store partial results. Internal structure of a calculator that leads to this functionality. Machine language and programs writing a sequence of instructions to evaluate arithmetic expressions. Computer or computing assistant in the traditional sense of the word, i.e., human operating a calculator. Interpreting the computer�s behavior when instructions are carried out: the fetch-decode-execute cycle as the basic or atomic unit of a computer�s function. Control unit: that performs the fetch-decode-execute cycle.

  2. Parts of a computer :
    Processor (CPU), memory subsystem, peripheral subsystem. The memory interface: memory subsystem minus the actual memory. Ditto with the peripheral interface. Parts of these interfaces integrated with the processor, and the remainder contained in the chip-set that supplements the processor. Two main parts of the processor apart from these interfaces: data-path (where computations take place) and control (which supervises the data-path) An important aim of the CO course is to understand the internals of these parts, and the interactions between them.

  3. Instruction set formats :
    Three-address and one-address instructions and the corresponding data-path architectures, namely, general-purpose register architecture (the classic RISC) and accumulator architecture. Zero-address instructions and the stack architecture. Two-address instructions, e.g., in the Pentium.

  4. Introductory Machine :
    Modern computer design, dating back to the 1980�s, marks a radical shift from the traditional variety. The new style has given rise to reduced instruction set computers (RISC), as opposed to the older complex instruction set computers (CISC). The Pentium is an instance of CISC, and hence is not considered in this course. The MIPS R2000, arguably the classic RISC machine, is the student�s first introduction to CO.

  5. Basic Electronics :
    Just those concepts needed to understand CO: combinational functions and their implementation with gates and with ROM�s; edge-triggered D-flip-flops and sequential circuits; Implementation of data-path and control, using the basic ideas developed so far.

  6. Memory hierarchy :
    Performance tradeoffs: fast, small, expensive memories (static RAM); slower, larger, inexpensive memories (DRAM); very slow, very large and very cheap memories (magnetic and optical disks).
    Ideal memory: fast, inexpensive, unbounded size. Ways of creating illusions or approximations of ideal memory. On-chip and off-chip cache memories, redundant arrays of independent disks (RAID).

  7. Pipelining :
    Improving the performance of a computer and increasing the usage of its subsystems by executing several instructions simultaneously. Analogy to assembly line manufacture of cars. Influence of instruction set design on ease of pipelining. Difficulties with pipelining: structural, data and branch hazards. Branch prediction.

  8. Peripherals :
    Interconnecting peripherals with memory and processor.

  • Bibliography
    Computer Organization and Design, Patterson and Hennessey
    Computer Structures, Ward and Halstead
    Digital Design: Principles and Practices, Wakerley

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CS-103 Mathematical Foundations
  • Aims

  1. Constructivity: A view of mathematics as programming

  2. Impredicativity: Limitations of constructivity

  3. Formality: Habits of mathematical style: ''Definition... Theorem... Proof...''

  4. Informality: Benefits and limitations of the above: Formality understanding

  5. Notation: Use, elision and invention of appropriate notation

The following is too vast for a course. The instructor may select from it in keeping with the above aims and objectives and to achieve a smooth integration with the IP course.

  • Contents

  1. Logic Propositional Calculus: Alternative styles: Boolean Algebra, truth tables, equational, deduction, Formal systems, Syntax and semantics, Proof theory and Model theory, Consistency and Completeness of different systems.

  2. Selfreference, paradoxes, Godel's theorem Alternative Logics eg. modal, dynamic, intuitionistic, situational Applications: Prolog, Program Verification

  3. Binding Constructs:
    Abstraction of lambda, for all, program function etc. Free and bound variables, substitution. Common laws.

  4. Set Theory:
    Definitions, proofs, notations, building models
    Applications: Z, Abrial's machines

  5. Wellformed formulae:
    Ordinary definition, refinement to types, necessity and limitation of computable type checking.

  6. Category Theory:
    Problems with Set theory constructive, conceptual and type and their categorical solution Applications: functional programming equivalents of categorical results

  7. Relations:
    3 alternative views of foundations of relations: as cartesian products, as boolean functions (predicates), as powerset functions 3 basic types - equivalences, orders, functions - properties and applications Applications in databases

  8. Calculus (Closely integrated with IP)
    Explicit and Implicit definitions. The 3 ingredients of function definition: naming, abstraction/quantification, property/predicate.

    Mathematically - separates the 3
    Computationally - delays by transforming computation into recipies
    Philosophically - enriches the programmer's world by moving programs from syntax to firstclass semantics

  9. Algebraic Structures:
    Development: Logic, Set Theory, Cartesian Products, Relations, Functions, Groupoids, Groups, Manysorted Algebras
    Lattice Theory
    Applications to cryptography, denotational semantics, cryptography

  • Bibliography : Logic for CS by Gallier
    Discrete Maths by Tremblay Manohar
    Discrete Maths by Stanat
    Laws of Logical Calculi by Morgan
    Category Theory tutorial by Hoare
    Category Theory by Burstall and Rydheard
    Computer modelling of mathematical reasoning by Bundy
    Shape of mathematical reasoning by Gasteren
    Predicate Calculus and Program Semantics by Dijkstra
    Algebra of Programming by Richard Bird
    Functional Programming with Bananas, Lenses and Barbed Wire by Fokkinga.
    http://wwwhome.cs.utwente.nl/�fokkinga/#mmf91m
    A Gentle Introduction to Category Theory the calculational approach by Fokkinga
    http://wwwhome.cs.utwente.nl/�fokkinga/#mmf92b
    A Logical Approach to Discrete Math by Gries and Schneider
    Practical Foundations of Mathematics by Paul Taylor
    Conceptual Mathematics by Lawvere
    Practical Foundations of Mathematics by Taylor
    Internal Documents of R.P.Mody on notation, style, combinato

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CS-104 Concrete Maths and Graph Theory
  • Aim
    The aims of this course are to enable the student to

  1. Obtain mathematical formulations of real world combinational problems

  2. Solve them algorithmically

  3. Do simple analysis of efficiency of such algorithms

  4. Acquire the necessary mathematical background for doing deeper analysis of algorithms

    At the end of the course the student should be familier with

  1. The notion of a graph and the related concepts

  2. Algorithms to solve various graph theoretic problems

  3. Idea of efficiency of an algorithm and simple methods of estimating computing time of various algorithms

  4. tools of algorithm analysis such as solution of recurrence relations, asymptotic notation etc.

  • Contents
    Graph Theory

  1. Graphs
    Definition and examples of graphs
    Incidence and degree, Handshaking lemma, Isomorphism
    Subgraphs, Weighted Graphs, Eulerian Graphs, Hamilitonian Graphs
    Walks, Paths and Circuits
    Connectedness algorithm, Shortest Path Algorithm, Fleury's Algorithm
    Chinese Postman problem,Traveling Salesman problem

  2. Trees
    Definition and properties of trees
    Pendent vertices, centre of a tree
    Rooted and binary tree, spanning trees, minimum spanning tree algorithms
    Fundamental circuits, cutsets and cut vertices, fundamental cutsets, connectivity and separativity, maxflow mincut theorem

  3. Planar Graphs
    Combinational and geometric duals
    Kuratowski's graphs
    Detection of planarity, Thickness and crossings

  4. Matrix Representation of Graphs
    Incidence, Adjacency Matrices and their properties

  5. Colouring
    Chromatic Number, Chromatic Polynomial, the six and five color theorams, the four color theoram

  6. Directed Graphs
    Types of digraphs, directed paths and connectedness, Eular digraphs, Directed trees, Arborescence, Tournaments, Acyclic digraphs and decyclication

  7. Enumeration of Graphs
    Counting of labeled and unlabeled trees, Polya's theoram, Graph enumeration with Polya's theoram

  • Concrete Mathematics

  1. Sums
    Sums and recurrences, Manipulation of sums, Multiple Sums, General methods of summation

  2. Integer Functions
    Floors and ceilings, Floor/Ceiling applications, Floor/Ceiling recurrences, Floor/Ceiling sum

  3. Binomial Coefficients
    Basic Identities, Applications, Generating functions for binomial coefficients

  4. Generating Functions
    Basic maneuvers, Solving recurrences, Convolutions, Exponential generating functions

  5. Asymptotics
    O notation, O manipulation, Bootstrapping, Trading tails

  • Bibliography
    Graph Theory with Applications, Bondy, J. A. & U. S. R. Murty [1976], MacMillan
    Graph Theory with Applications to Engineering and Computer Science, Deo, Narsing [1974], Prentice Hall
    Concrete Mathematics, A Foundation for Computer Science, Graham, R. M., D. E., Knuth & O. Patashnik [1989], Addison Wesley
    Notes on Introductory Combinatorics, Polya, G. R. E. Tarjan & D. R. Woods [1983], BirkHauser
    Graph, Networks and Algorithms, Swamy, M. N. S. & K. Tulsiram [1981], John Willey

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CS-105 Numerical Methods
  • Aims
    The stress in teaching Numerical Analysis should be to treat is as a mathematical discipline and not as an art. The stress should be on teaching unifying principles and to avoid teaching them as a bag of tricks. As such, the syllabus given below must be in followed in the order given below. Failure to follow this suggestion may result in Numerical Analysis slipping back into an unattractive subject.

    An Algorithm as a computational procedure should be differentiated from the theorem, which is a statement about what the Algorithm does.

    As many of our students are likely to come with a poor knowledge of complex variables, it is necessary to give an introduction to complex variable theory as part of numerical analysis course.

    We have omitted the conventional topics of numerical differentiation, numerical integration and numerical solution of ordinary differential equations. The argument in favour of these omissions is that these subjects involve an initial step of approximating the specific continuous process by replacing it by the approximating polynomial, difference equations or systems of equations . ( These amount to modelling the continuous system by a discrete system ).

    Next we proceed to work on these derived models. Our syllabus contains all the above mentioned topics needed for modelling. We feel that the topic of modelling should be kept out of our syllabus as it forms a major subject by itself. A casual mention about the methodologies of modelling mentioned above should be included for the students to realise that they have to learn it as a separate subject when necessary.

    With the above preliminaries we now list the topics of the syllabus with text books to guide us through.

  • Contents :

  1. Introduction to Complex Variable theory

  2. Matrix Algebra

  3. Numerical Solution of Linear Equations. Direct Methods and Iterative Methods. Eigen value and Eigen vector calculation.

  4. Solutions of Systems of Nonlinear Equations

  5. Iteration : Convergence of iteration, Error, Accelerating Convergence, Aitkin's Method,Quodiotic Conveyance, Newton's Method, Diagonal Aitken's Method.

  6. Iteration for system of equations: Contraction Mapping, Lipschitz Condition, Quadratic Convergence, Newton's Methods, Bairstow's Method. Linear

  7. Difference Equations : Particular solution of Homogeneous Equation of order two, General Solution, Linear Dependence, Non Homogeneous Equation of order two, Linear Differnce Equation of Order N, System of Linearly independent Solutions.

  8. Propagation of roundoff error

  9. Interpolation and approximation
    Interpolating Polynomials, Existence, Error and Convergence of Interpolating. Polynomial Constuction of Interpolating Polynomials from ordinates and by using differences.

    Notes :
    The course will start by teaching Complex Variable Theory and asking the students to read the Matrix Algebra by themselves. This will be followed by a test or these topics. The remaining topics will now be covered more or less in the same order as listed in the syllabus.

  • Bibliography
    Elements of Numerical Analysis, Peter Henrici, John Wiley & Sons.
    Numerical Linear Algebra, Leslie Fox, Oxford University Press.
    Lecture Notes on Numerical Analysis, R. Sankar

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CS-201 Data Structures and Algorithms
  • Aims
    To distinguish between and be able to relate the high level ( mathematical ) world of data structures and the low level ( engineering ) world of storage structures. To develop a vocabulary for algebraic manipulation of data structures and a calculus of systematic refinement to algorithms and storage structures in the low level world of C and machines.

    To round off the foundations laid in IP and MF by engineering slightly bigger software on realistic computer systems.

  • Prerequisites
    Introduction to Programming, Mathematical Foundation & Logical Organization of Computers

  • Course Overview

      Algebraic view Algorithmic view
    Data Data Structures, Mathematical Definitions, Laws, Manipulations, MF relations Storage Structures, Engineering Considerations related to CO, LLP
    Code Recursive and closed form program specification. May be implementable in a high level language like gofer or may not be implementable directly. The intrinsic value of specification apart from programs. Explicit control through built in control structures like sequencing, if, while Engineering efficient implementation of correct specifications


  • Contents
    The course is organized according to the philosophy in the table below. The case studies/examples include but need not be limited to

    Lists: Various types of representations.
    Applications: symbol tables, polynomials, OS task queues etc

    Trees: Search, Balanced, Red Black, Expression, and Hash Tables
    Applications: Parsers and Parser generators, interpreters, syntax extenders

    Disciplines: Stack, queue etc and uses

    Sorting and Searching: Specification and multiple refinements to alternative algorithms

    Polymorphic structures: Implementations (links with PP course)

    Complexity: Space time complexity corresponds to element reduction counts. Solving simple recurrences.

  • Course Organization

      Algebraic world Algorithmic world
    Correctness Bird Laws, Category Theory Refinement, Predicates
    Transformation Via Morgan Refinement
    ADTs and Views
    • Formulation as recursive data types
    • Data structure invariants
    • Principles of interface design
    • Algebraic Laws
    • C storage
    • Representation Invariants
    • Addressing Semantics
    • Use of struct, union and other assorted C stuff
    • Maximizing abstraction by macros, enums etc
    Mapping Via transforms and coupling invariants
    Code
    • Pattern Matching based recursive definitions
    • Exhaustive set of disjoint patterns correspond to total functions
    • Correspond to runtime bug free programs
    • Recursive Code structures follow from recursive data structures
    • Refinement of recursive definitions into iterative algorithms
    • Techniques (Bentley) for improving algorithms e.g. sentinel, double pointers, loop condition reduction,strength reduction etc.
    Continuations
    • Control as Data
    • Co routines vs. subroutines
    • General framework for escape procedures, error handling
    • Loops
    • Functions @
    • Stack based software architecture
    Error Policy Types
    • Patterns
    • Laws
    • Deliberate Partiality
    Predicate Transformer Semantics for control
    Modules Category Theory Files, make

     

  • Bibliography
    Data Structures and Algorithms, Aho, Hopcroft and Ullman, Addison Wesley Inc.
    Data Structures, Kruse, Prentice Hall
    Programming from Specifications, Carroll Morgan, Prentice Hall
    Algebra of Programs, Bird, Prentice Hall
    Programming Perls, Writing Efficient Programs, John Bentley, Prentice Hall
    Structure and Interpretation of Computer Programs, Abelson Sussmann, MIT Press
    Functional Programming Henderson, Prentice Hall
    The Art of Programming Vol. 1. & Vol. 3, D. E. Knuth, Addison Wesley Inc

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CS-202 Theoretical Computer Science
  • Aims

  1. To question informal techniques in programming bu inverting them into questions of programmability (computability). This can be achieved by exploring issues in computation and linking it with the logics of proff/decidability.

  2. To acquint with (i) Complexity (ii) Semantics and in this context systematically show the increase in power of Lower power formalisms, languages, automata in the direction of its very limits the notion of computability.

  3. To reformulate old, classical definitions of computability in the technologically relevant setting of modern programming languages, thus breaking the theory Vs practice divide.

  4. To build a conceptual glue that spans the triad automata, languages and computation.

  • Prerequisites
    Introduction to Programming, Mathematical Foundation.

  • Contents

  1. Low Power Formalisms Combinational Machines inadequacy

  2. FSM as acceptor, generator, regular expressions and equivalence

  3. PDA brief idea, relation between CFG's and programming languages (informal)

  4. Full Power Mechanisms
    (i) Recursive functions
    (ii) Turing machines cost models for the RAM
    (iii)Post systems/Lambda Calculas/Markov algorithms
    (iv) (any one) Use must be stressed along with mutual equivalences.
    Any of the (iii) should be done so as to give a theoretical backing to the practical notion of 'nonVonNeumann' language.

  5. Self References :
    Use mention distinctions, 'escape methods' for selfreferencing quines, selfreferences in the expression domain, the formulation of the 'halting problem' and decidability in C and Scheme

  6. Recursive Data :
    Recursive, Enumerable sets, generators and recognisers formulated as recursive types in Haskell, 'S' expressions in Scheme.

  7. Complexity Basic ideas measuring time usage, time hierarchies

  8. Deterministic and Nondeterministic computations.

  9. Ability od a mechanism to solve a problem. Formalization of the problem. Chruch Turing thesis.

  10. Universality

  11. Equations in language spaces
    Operational approach
    Denotational approach

  • Bibliography
    Introduction to the theory of computation, Sipser, Thompson Learning
    Computabilities and complexity from a programming perspective, Niel Jones, MIT Press
    The Quine page, Gary P. Thompson, at http://www.myx.net/�gthompso/quine.htm
    Computation and Automata, Salomaa, CUP
    Switching and finite Automata Theory, Kohavi, ZVI, Tata McGrawHill
    Finite and Infinite Machines, Minsky, Prentice Hall
    Post Systems, Krishnamurthi E. V.
    Godel, Escher, Bach, Hoffstader, Vintage Books
    Introduction to Recursive Function theory, Cutland, CUP
    Handbook of TCS Vol A,B, Jan Van Leeuvven ed, Elsevier

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CS-203 Low-Level Programming
  • Aims
    Modern Computer Systems have layer upon layer of s/w abstractions. These abstractions, though perhaps made with the best intentions, ultimately end up obscuring the actual workings of computers. The primary aim of the LLP course is to crack open this high level abstraction layer.

  • Prerequisites
    Computer Organization, Introduction to Programming

  • Objectives
    To understand the workings of a computer at the lowest levels where h/w and s/w meet.
    C Programming (Basics here, advanced in Data structures and syspro).
    To be able to write assembly language programs (in small doses) and to integrate C and assembly (in larger doses).
    To be comfortable with low level system software.
    The above to be done with respect to a specific OS depending on availability and instructors choice.

    Note: Originally this course was conducted entirely within MS�DOS. But with DOS almost dead and other OS's not quite as convenient for low level hacking, there is some problem with infrastructure, and course material.

  • Contents

  1. C Language Basics

  2. Assembly Language structure, syntax, macros

  3. Use of linker, librarian, object editor(s), debugger

  4. C Assembly Interfacing coding conventions, translations of arrays, structs, call return sequences. Mixed code.

  5. 8086 architecture going up to P4. Survey of Intel architecture history

  6. Machine language programing: Assembling and disassembling, clock cycle counting, instruction length calculation. Philosophy and history of instruction format choices. Combinatorial considerations and limitations.

  7. I/O Classification: Memory mapped vs IP mapped. Polled, Interrupt, DMA

  8. Interrupts: h/w and s/w. ISRs. Assembly and C. Minimization and handling of non determinism Separation of binding times: Hardcodings of chip, board, OS, system s/w,user levels

  9. OS use: system call interface

  10. OS implementation: Start up scripts, Basics of protected mode and device drivers
    Chip Level Programming

  • Bibliography
    Art of Assembly, Randy Hyde
    Intel Manuals
    OS, chip manuals
    Compiler and System S/w manuals
    C Programming, Kernighan and Ritchie

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CS-205 Computer Architecture and Operating Systems
  • Aims:
    To describe the major architechtural styles of computer systems and the programmed abstract machine that is created over a given computer system via operating systems software.

  • Prerequisites :
    Computer Organization, Introduction for Programming

  • Contents :
    Register Transfer model of processors. Data paths and control structures. Comparison of architechtural styles for general purpose computers including RISC/CISC. Pipelining hazards and their resolution. Storage hierarchy in a computer: caches and virtual memory

  • I/O systems:
    Polled and interrupt driven interfaces. Machine level devices like disks and serial/parallal ports, user level devices like keyboards and video units.

    Simple computer systems made up of a single processor and single core memory spaces and their management strategies. Processes as programs with interpolation environments. Multiprocessing without and with IPC. Synchronization problems and their solutions for simplecomputer systems. Memory management: segmentation, swapping, virtual memory and paging. Bootstraping issues. Protection mechanisms.

    Abstract I/O devices in Operating Systems. Notions of interrupt handlers and device drivers. Virtual and physical devices and their management.

    Introduction to Distributed Operating Ststems. Architechture designs for computer systems with multiple processors, memories and communication networks. Clocking problem and Lamport's solution.

    Illustrative implementation of bootstrap code, file systems, memory management policies etc.

  • Bibliography
    D. A. Patterson & J. L. Hennessy, Computer Organization and Design: the hardware/software interface, MorganKaufmann.
    A. S. Tanenbaum, Structures Computer Organization, 3rd edition, PrenticeHall
    J. L. Hennessy & D. A. Patterson, Computer Architechture: a quantitative approach, MorganKaufmann
    A. S. Tanenbaum, Modern Operating Systems, PrenticeHall
    A. S. Tanenbaum, Distributed Operating Systems, PrenticeHall
    M. Singhal & N. Shivaratri, Advanced Concepts in Operating Systems, McGrawHill

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CS-206 Programming Paradigms
  • Aim :
    When students first study programming, the one style that they have learnt, they inevitably take to be THE style. The aim of the PP course is to convert that 'THE' into 'a'.

  • Prerequisites :
    Introduction to Programming

  • Objectives :
    A variety of different ways of thinking about programming are presented. The differnces are investigated so that the word 'paradigm' can begin to make sense the different languages covered are vehicles, not the goal.
    The sense of the intellectual content for computer science as being not fixed but a melting pot of new ideas.
    Some aspects of how these paradigms are implemented.
    Note : Certain commonly covered paradigms such as functional paradigms are not here because they are integrated into the programming mainstream.

  • Contents

  1. GUI Programming

  2. GUI Vs CUI

  3. Event Driven Programming

  4. Visual (Meta-GUI) Programming

  5. Architechture of typical Application

  6. VB Environment : Steps in creating and using controls

  7. Database Connectivity, codeless programming

  8. OO Paradigm

  9. Modularity

  10. Data Abstraction

  11. Classes and Objects

  12. Inheritance and interfaces

  13. Polymorphism

  14. Inner Classes

  15. Use of AWT and Swing for GUIs

  16. Applets (if time permits)

  17. UML: Class Diagrams, Sequence Diagrams

  18. UML to Java tools (ArgoUML)

  19. HDL via Verilog or VHDL

  20. Architechtural behavioral and RT levels

  21. Study of Waveforms

  22. Differences between features used for testing and allowable in design

  23. Notion of Scripting

  24. Scripting via Perl/Guile/Python

  • References
    Verilog HDL by S. Palnitker (Prentice Hall)
    Perl by Wall and Chistiansen (O'reilly)
    Core Java 2 Vol I fundamentals and Vol II Advanced features by Cay S. Horstmann and Gery Cornell (Prentice Hall)
    Thinking in Java Vol 3 by Bruce Eckel at http://www.mindview.net/books/TIJ
    Scripting reference at http://home.pacbell.net/ouster/scripting.html
    Guile for scripting at http://gnuwww.epfl.ch/software/guile/guile.html
    The art of programming with Visual Basic by Mark Warhol (John Wiley & Sons)
    Visual Basic 6.0 programmer's guide (Microsoft Press)
    Visual Basic 6.0 database programming bible by Wayne Freeze (Hungry Minds)
    Dive into Python by Mark Pilgrim at http://diveintopython.org
    Programming Python by Mark Lutz, 2nd Edition (O'Reilly)
    Python Docmentation at http://www.python.org/doc/

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CS-204 Design and Analysis of Algorithms

  • Aim
    This course focuses on fundamental techniques for the design and analysis of correct and efficient algorithms. After reviewing the applicable mathematics and introducing the basic concepts, the course presents several design techniques. First a technique is introduced in its full generality, and then it is illustrated by concrete examples drawn from several different application areas. Attention is given to the intregation of the design of an algorithm with the analysis of its efficiency and correctness. The course also introduces the concepts of computational complexity.

  • Prerequisites :
    Graph Theory and Concrete Mathematics, Data Structures and Algorithms

  • Contents :

  1. String processing

  2. KnuthMorrisPlatt Algorithm, BoyerMoore Algorithm, pattern Matching.

  3. Graph and geometric Algorithms

  4. DFS, BFS, Biconnectivity, all pairs shortest paths, strongly connected components, network flow

  5. FordFulkerson Algorithm, MPN Algorithm, Karzanov Algorithm, Maximum Matching in bipartic graphs

  6. Geometric Algorithms

  7. Backtracking, Dynamic Programming, Branch & Bound, Greedy

  8. Use of three paradigms for the solution of problems like Knapsack problem, Traveling Salesman etc.

  9. Lower Bound Theory

  10. Sorting, Searching, Selection

  11. Introduction to the theory of NonPolynimial Completeness NonDeterministic Algorithms, Cook's Theoram, clique decision Problem, Node cover decision problem, chromatic number, directed Hamiltonian cycle, traveling salesman problem, scheduling problems.

  • Bibliography
    Introduction to Algorithms, Cormen, Leiserson, Rivest, MIT Press and McGraw Hill, 1990 Algorithms, Robert Sedgwick, Addison Wesley Publishing Company, 1988
    The Design and Analysis of Computer Algorithms, A. V. Aho, J. E. Hopcroft, J. D. Ullman, Addison Weslay, Reading, Mass, 1974
    Computer Algorithms: Introduction to Design & Analysis, Sara Baase, Allen Van Gelder, Addison Wesley Pub. Co., 2000
    Computer Algorithms, Sara Baase, Addison Wesley, 1988
    Combinational Algorithms (Theory and Practice) , F. M. Reingold, J. Nivergelt and N. Deo, Prentice Hall Inc., Engiewood Cliffs, N. J., 1977
    Combinational Algorithms, T. C. Hu, Addison Wesley, 1982

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CS-301 Database Management System
  • Aim :
    Concepts in DBMS are taught in depth. The student will attain the ability to design Databases and understand the ACID principles and nittygritty involved in any DBMS development, through the implementation excercises carried out in the course.

  • Prerequisites :
    Data Structures and Algorithms

  • Contents
    DBMS objectives and architechtures

  1. Data Models
    Conceptual model, ER model, object oriented model, UML Logical data model, Relational, object oriented, objectrelational

  2. Physical data models
    Clustered, unclustered files, indices(sparse and dense), B+ tree, join indices, hash and inverted files, grid files, bulk loading, external sort, time complexities and file selection criteria.

  3. Relational database desing
    Schema design, Normalization theory, functional dependencies, lossless join property, join dependencies higher normal forms, integrity rules, Relational operators, relational completeness, Relational algebra, Relational calculas

  4. Object oriented database design
    Objects, methods, query languages, implementations, Comparison with Relational systems, Object orientation in relational database systems, Object support in current relational database systems, complex object model, implementation techniques

  5. Mapping mechanism
    conceptual to logical schema, Key issues related to for physical schema mapping

  6. DBMS concepts
    ACID Property, Concurrency control, Recovery mechanisms, case study Integrity, Views & Security, Integrity constraints, views management, data security

  7. Query processing, Query optimization -
    heuristic and rule based optimizers, cost estimates, Transaction Management

  8. Case Study
    ORACLE/POSTGRES DBMS package: understanding the transaction processing Concurrency and recovery protocols, query processing and optimization mechanisms througn appropriate queries in SQL and PLSQL.

  9. Web based data model -
    XML, DTD, query languages, Xpath, Xquery

  10. Advanced topics
    Other database systems, distributed, parallel and memory resident, temporal and spatial databases.

  11. Introduction to data warehousing, OnLine Analytical Processing, Data Mining.

    Bench marking related to DBMS packages, database administration

  • References
    Database System Concepts, Silberschatz, Korth and Sudershan, McGraw Hill Company
    Database Management Systems, Raghu Ramakrishnan, Johannes Gehrke, 2002
    Principles of Database Systems Vol. I & Vol II, J. D. Ullman, Rockville, MD: Computer Science Press, 1998

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CS-302 Computer Networks
  • Aim :
    General principles and concepts of computer networks and the services built on top of them are covered. The student will attain the ability to design basic network services and implement network Systems.

  • Prerequisites :
    Computer Architechture & Operating Systems, Data Structures and Algorithms

  • Contents :

  1. Network architechture, ISO-OSI Reference model

  2. Network Topology:

  3. Topology design problem, connectivity analysis, delay analysis, backbone design, local access network design.

  4. Physical Layer, Transmission media, digital transmission, transmission & switching,

  5. Intregrated Services Digital Network.

  6. Data Link Layer: Design issues, protocols, CRC

  7. Network Layer: Design issues, routing algorithm, congestion control, Packet switched networks,

  8. X.25 standards, ATM networks

  9. Transport Layer: TCP, UDP, Design issues

  10. Session Layer: Design issues, client server model, remote procedure calls
    Local Area Networks, IEEE 802 standards for LAN (Ethernet, token ring, optical fiber, wireless)

  11. Application layer environment

  12. Application layer architechture, building applications with sockets, DNS, HTTP, SMTP, LDAP, NFS, NIS, SNMP, WAP Mobile computing

  13. Internet, extranet, Virtual Private Network (includes tunneling, internet work routing and fragmentation)

  14. Internet Security: Firewalls, SSL, Popular encryption protocols

  • References
    Data and communications, 6th Edn., W. Stallings, Prentice Hall, 2000
    Computer networks: A systems approach, 2nd Edn., Peterson and Davie, Morgan Kaufman
    Computer Networks, 4th Edn., A. S. Tanenbaum, Prentice Hall
    UNIX Network Programming: Interprocess Communications, Stevens , Prentice Hall, 1999

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CS-303 Systems Programming
  • Aims:
    To convey the idea that systems programs help in building an abstract machine over the raw machine, and are governed by the fundamental need of interpretation. Also attempt to demonstrate the mix of techniques from formal to heuristics that are used to write real programs.

  • Prerequisites
    Computer Architechture and Operating Systems, Theoretical Computer Science, Data Structures and Algorithms

  • Contents :

    The four dimensions of a programming activity as the basis for systems programming: concept, program generators (humans or other programs), sources and deliverables. For a variety of concepts, a set of program generators generate a set of (possibly overlapping) sources and produce a set of deliverables (executables, libraries, documentation).

    Interpretation as the fundamental activity in Software. Interpreters and interpretation. Program layout strategies on a Von Neumann machine (e.g. Pentium). Divison of the final interpretation goal into subtasks and establishing interface export by producer tool and import by consumer tool.

    Linkers and Loaders
    Linker as a layout specifying producer and loader as a layout specification consumer. Layout specification strategies: fixed and variable (relocatable and selfrelocatable). Layout calculations. Dynamic linking and overlays. Executable format definitions. Object file format as the interface betwen the compiler and the linker. Few Object file formats like MSDOS, Windows and ELF. Object file manipulation utilities. Source files related system software. Syntax manipulation (lex and yacc). Editors, version controllers. Version control on object and executable files (e.g. version support for modules in the linux kernal).

    Support tools:
    Literate programming (weave, tangle), source browsers, documentation generators, make, GNU autoconf, CVS, bug reporting systems. IDEs for systematic use of system tools. Flow graphers, Debuggers for analysis. Package builders, installers, package managers for deployment

    The notion of binding time as instant of achieving the mapping between a symbol and a value. Overlays and remote procedure call as memory space influenced between symbol and value.

  • References :
    Hopcroft, Sethi and Ullman, Compiler Principles, AddisonWesley
    John Levine, Linkers and Loaders, http://www.iecc.com
    info lex and info bison on GNU/Linux Systems
    H. Abelson and G. Sussmann, Structure and Interpretation of Computer Programs (SICP), MIT Press
    Hopcroft and Ullman, Introduction to Automata theory, Languages and Computation, Narosa Publishing
    The details of the Pentium can be found in various manuals at ftp://developer.intel.com.design/Pentium4/manuals/
    Basic Architechture: 24547012.pdf. Instruction Reference: 24547112.pdf
    System Programming Guide: 24547212.pdf

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CS-305 Computer Graphics
  • Aims
    Equip the student with the tools and techniquies required for the generation and manipulation of pictures/images. The device independent aspects as well as the device dependent quirks of color and gray scale devices is expected to be appreciated by the student at the end of this course.

  • Prerequisite(s)
    Students opting for this course should have a certain degree of programming experience. IP and DSA or their equivalent competence should suffice.

  • Contents
    Introduction, Image Processing as Picture Analysis and Computer Graphics as Picture Synthesis, Representative Uses of Computer Graphics, Classification of Applications.

    Raster Graphics Features, raster algorithms including primitiveslikelines, circles, filling, clipping in 2D, etc.

    Geometric transformations in 2D for 2D object manipulation, coordinate transformations and their matrix representation, Postscript language to demonstrate these concepts.

    The 3rd dimension, it's necessity and utility, transformations and modelling in 3D, geometric modelling with an introduction to curves and surfaces for geometric design, including but not restricted to Bezier, B�spline, hermite representations of curves and surfaces

    From 3D back to 2D projections, hidden surface elimination and the viewing pipeline. Achromatic Light, Chromatic Color, Color Models for Raster Graphics, Reproducing Color, Using Color in Computer Graphics

    Rendering Techniques for Line Drawings, Rendering Techniques for Shaded Images, Aliasing and Antialiasing, Illumination Models local models like Phong, CookTorrance and global models likeraytracing and radiosity, shading detail like textures, their generation and mapping, bump mapping and similar techniques.

    Depending on time availability, one of volume rendering, modelling of natural objects, introduction to 3D animation may be covered depending on student and instructor inclination

  • References
    Computer Graphics: Principles and Practice, J. Foley, A.van Dam, S. Feiner, J.Hughes, Addison Wesley Pub., 1997
    Computer Graphics, D. Hearn, M. P.Baker, Prentice Hall, 1997
    Computer Graphics, F. S. Hill Jr., Macmillan Pub, 1990
    Curves and Surfaces for Computer Aided Geometric Design, 4th Edn., G. Farin, Academic Press, 1997
    Mathematical Elements for Computer Graphics, 2nd Edn., D. Rogers, McGraw Hill Pub., 1990
    The Mathematical Structure of Raster Graphics, E. Fiume, Academic Press, 1989
    Graphics Gems , Vol. 15, Academic Press
    The Rendering Equation, J. Kajiya, SIGGRAPH 1986, 143�150

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CS-401 Modeling and Simulation
  • Aims
    Introduce the student to practical/realworld systems which require understanding and defy complete (if any) analytical methods towards their analysis and hence the requirement for modelling and simulation. This will include the mathematical, statistical and language tools required for specifying a model, wunning the simulation and analysing the results. The course would emphasize DES while showing linkages to other types of simulation.

  • Prerequisites
    Introduction to Programming, Data Structures and Algorithms (at the discretion of the instructor)

  • Contents :

  1. Introduction to Systems modelling concepts, contimous and discrete formalisms

  2. Framework for Simulation and Modelling, modelling formalisms and their simulators, dicrete time, contimous time, discrete ecevt, process based.

  3. Hybrid systems and their simulators

  4. Review of basic probability, probability distributions, estimation, testing of hypotheses

  5. Selecting input probability distributions, models of arrival processes

  6. Random number generators, their evaluation, generating random variates from various distributions.

  7. Output analysis, transient behaviour, steadystate behaviour of stochastic systems, computing alternative systems, variance reduction techniques.

  8. Verification and Validation

  • References
    Discrete Event System Simulation, 3rd ed., J. Banks, J. Carson, B. Nelson, D. Nicol, Prentice Hall Pub., 2001
    Simulation Modelling and Analysis, 3rd ed., A. Law, W. Kelton, McGraw Hill Pub., 2000
    Simulation with Arena, 2nd ed., W. Kelton, R. Sadowski, D. Sadowski, McGraw Hill Pub., 2002
    Theory of modelling and Simulation, 2nd ed., B. Zeigler, H. Praehofer, T. Kim, Academic Press, 2000
    Object Oriented Simulation with Hierarchial Modular Models, B. Zeigler, Academic Press, 1990
    Reality Rules, Vol. I and Vol. II, J. Casti, John Wiley Pub., 1996

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CS-402 Operations Research
  • Aims
    This course will focus on two aspects of OR, viz. deterministic (mathematical programming) and stochastic. At the end of the course the student should have developed or honed his/her skills at modelling (or at least problem formulation) and be able to choose an appropriate quantitative technique towards it's solution, or be aware that the problem on hand is *not* ammenable to quantitative techniques.

  • Prerequisite(s):
    No course related prerequisite but a background in basic differential and integral calculus is asumed along with familiarity with matrix algebra.

  • Contents:

  1. The nature of O.R., History, Meaning, Models, Principles Problem solving with mathematical models, optimization and the OR process, descriptive vs. simulation, exact vs. heuristic techniques, deterministic vs. stochastic models.

  2. Linear Programming, Introduction, Graphical Solution and Formulation of L.P.Models, SimplexMethod (Theory and Computational aspects), Revised Simplex, Duality Theory and applications Dual Simplex method, Sensitivity analysis in L.P., Parametric Programming, Transportation, assignment and leastcost transporation, interior point methods: scaling techniques, log barrier methods, dual and primaldual extensions

  3. Introduction to game theory

  4. Multiobjective optimization and goal programming

  5. Shortest paths, CPM project scheduling, longest path, dynamic programming models

  6. Discrete optimization models: integer programming, assignment and matching problems, facility location and network design models, scheduling and sequencing models

  7. Nonlinear programming: unconstrained and constrained, gradient search, Newton's method,

  8. NelderMead technique, KuhnTucker optimality conditions. These topics should only be covered only time permits.

  9. Discrete Time processes: Introduction, Formal definitions, Steady state probabilities, first passage and first return probabilities, Classification terminology,Transient processes, queing theory introduction, terminology and results for the most tractable models like M/M/1

  10. Inventory Models ( Deterministic): Introduction, The classical EOQ, sensitivity analysis, Nonzero lead time, EOQ with shortages, Production of lot size model, EOQ with quantity discounts, EOQ with discounts, Inventory models ( Probabilistic): The newshoy problem : a single period model, a lot size reorder point model

  • References
    Operations Research: An Introduction, 7th Edn., H. Taha, Prentice�Hall, 2002
    Operations Research: Principles and Practice, A. Ravindran, D, Phillips, J Solberg, John Wiley Pub, 1987
    Linear Programming and Extensions, G Dantzig, Princeton University Press, 1963
    Theory of Games and Economic Behaviour, J. von Neumann, O. Morgenstern, John Wiley Pub. 1967
    Goal Programming: Methodology and Applications, M. Schniederjans, Kluwer Academic Pub, 1995

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CS-403 System Analysis and Design
  • Aims
    Software development is a complex process. Good quality software results by using disciplined and methodological approaches for requirement analysis, design and coding. In this course, the student is introduced to both formal and less formal techniques used for requirement and domain analysis. At the end of the course the student will

    Become more adapt in understanding a problem in terms of its processes and concepts and design a good solution using CASE tools. Have sound understanding of software engineering issues for large scale development through modelling and notation and provide a foundation for the Software Engineering course (which is taught later), so as to be able to develop a piece of quality software according to sound principles and notation.

  • Prerequisites
    Mathematical Foundation, Programming Paradigms, exposure to DBMS is preferred.
  • Syllabus

  1. Introduction, Need, Software life cycles

  2. Overview of Requirements Engineering, Processes, the requirements document

  3. System Specification
    Logic Sets and Types, Z specification structure
    Relations, Functions, Sequences

  4. Structured System Analysis Design
    ER Diagrams, Data Flow Diagrams

  5. Object Oriented Softawre Design using UML

  6. Notations for Design
    A brief reintroduction to Object Oriented Concepts and an overview of the UML notation Characteristics of notations for design.

  7. Requirements Analysis
    User Requirements Gathering, Performing a Domain Analysis, Developing the Use Cases.

  8. System Specification
    Design and Analysis using UML
    Class Diagrams
    UML Activity Diagrams, Task Analysis
    UML Interaction Diagrams
    UML Object Diagrams
    UML Deployment Diagrams, Collaboration diagrams, Data Flow Diagrams

  9. SSAD Vs Object Oriented Design

  10. CASE Tools

  11. Forward Engineering and Reverse Engineering

  12. Code Construction
    UML to Code, Code to UML
    Z to Code

  • References
    The Engineering of Software, Dick Hamlet, Joe Maybee, Addison Wesley, 2001
    UML Distilled, 2nd Ed., Martin Fowler, Addison Wesley
    Introduction to the Personal Software Process, Watts S. Humphery, Addison Wesley, 1997
    Using UML for Software Engineering, Pooley and Stevens, Addison Wesley, 1999
    The Unified Modelling Language Users Guide, 1st Ed., Grady Booch, James Rumbaugh and Ivar
    Jacobdon, Addison Wesley, 1999
    Specification Case Study, Hayes, Prentice Hall
    Currie: The Essence of Z ISBN 013749839X, Prentice Hall
    UML Toolkit, Eriksson, John Wiley, 1998

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CS-304 Science Of Programming
  • Aims :
    To teach the use of formal methods in software development. Although formality should not be sacrificed too much, scaling up in software size with reduction in formality should be illustrated when possible .

  • Contents

  1. Verification : verification of imperative programs as in Gries/Dijkstra.

  2. Specific techniques : Invariant assertive method, subgoal induction method.

  3. Verification of pointer programs.

  4. Function Program verification: Induction on datatypes, infinite datastructure induction.

  5. Specification : Use of 'Z' as an modeltheoretic language.

  6. Clear as an example of a model axiomatic/categoric language.

  7. Transformation/Refinement

  8. Homomorphic transformations, refinement Calculus Theory & application of List/Functional

  9. Calculus

  10. Theory Logics of Programs

  11. Hoare Logics, Dynamic Logic

  12. Temporal Logic Application to OOP

  • Bibliography

    Functional Programming, Henson, Blackwell scientific
    Science of Programming, Gries, Narosa
    Discipline of Programming, Dijkstra, Prentice Hall
    Method of Programming, Dijkstra & Feijen, Addison Wesley
    Specification Case Studies, Hayes, Prentice Hall
    Software Specification, Gehani & Mcgettrick, Addison Wesley
    Program Specifications & Transformations, Meertens, Prentice Hall
    Partial Evaluation and Mixed Computation, Ershov, Bjorner & Jones, North Holland.
    Programs from Specifications, Morgan, Prentice Hall
    Lectures of constructive functional programming, Bird, Lecture notes, PRG Oxford
    Introduction to the theory of lists, Bird, Lecture notes, PRG Oxford
    A calculus of functions for program derivation, Bird, Lecture notes, PRG Oxford
    Introduction to Formal Program verification, Mili, Van Nostrand Reinhold

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CS-601 Software Engineering
  • Aims
    Software engineering is concerned with the cost effective development and evolution of software systems. This course introduces the topics through lectures and by giving the students a chance, in the form of a class project (which is a group project), to develop a software product and to manage its development process. It is a combination of the System Analysis and Design course and covers all other issues that sre needed in Software Engineering.

  • Prerequisites
    System Analysis and Design, Data Structures & Algorithms, Systems Programming and good technical writing skills.

  • Contents
  1. Concepts of software management, The software crisis, principles of software engineering, programming in the small Vs programming in the large

  2. Software methodologies/processes, The software life cycle, the waterfall model and variations, introduction to evolutionary and protyping approaches

  3. Software measurement

  4. Objectoriented requirements analysis and modeling: Requirements analysis, requirements

  5. solicitation, analysis tools, requirements definition, requirements specification, static and dynamic specifications, requirements review. (just revisited)

  6. Software architechture

  7. Software design, Design for reuse, design for change, design notations, design evaluation and validation

  8. Implementation, Programming standards and procedures, modularity, data abstraction, static analysis, unit testing, integration testing, regression testing, tools for testing, fault tolerance

  9. User considerations, Human factors, usability, internationalization, user interface, documentation, user manuals

  10. Documentation, Documentation formats, tools

  11. Project management, Relationship to life cycle, project planning,project control, project organization, risk management, cost models, configuration management, version control, quality assurance, metrics

  12. Safety

  13. Maintenance, The maintenance problem, the nature of maintenance, planning for maintenance

  14. Configuration Management

  15. Tools and environments for software engineering, role of programming paradigms, process maturity

  16. Introduction to Capability Maturity Model
    People Capability Meturity Model
    Software Acquisition Capability Maturity Model
    Systems Engineering Capability Maturity Model

  17. IEEE software engineering standards
    The course should consist of lectures and a weekly discussion section. Students should work in teams on problem analysis and other assignments during the discussion section. The lecture part of the course may include individual and group activities.

  • Bibliography

    Software Engineering, 6th Edn., Ian Sommerville, Addison Wesley, 2001
    (Note : This is also the preferred textbook for the IEEE Software Engineering Certificate Program.)
    The Engineering of Software,Dick Hamlet, Joe Maybee, Addison Wesley, 2001
    Introduction to the Team Software Process, Watts S. Humphrey, Addison Wesley, 2000
    Software Engineering A Practitioner's Approach European Adaption, 5th Edn., Roger S. Pressman, adapted by Darrel Ince, McGraw Hill, 2000
    Software Engineering Theory and Practice, Shari Lawrence Pfleeger, Prentice Hall, 1998
    Practical Software measurement, Bob Huges, McGraw Hill, 2000
    Human Computer Interaction, 2nd Edn., Dix, Finlay, Abowd and Beale, Prentice Hall, 1997
    Software Project Management, 2nd Edn., Bob Huges & Mike Cotterell, McGraw Hill, 1999



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