Dr.

Lavika Goel
Assistant Professor
Department of Computer Science and Engineering
Malaviya National Institute of Technology, Jaipur

CS C441 / CS F441 Second Semester 2013-14 BITS Pilani, Hyderabad Campus

Today’s Agenda

• General course information

• Objectives

• Course Logistics

• Course outline

• Introduction to Programming Methodology

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General Course Information

Course Coordinator: Dr. Lavika Goel

Office: First Floor, Dept. of CSE

Email: lavika.cse@mnit.ac.in

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Course Motivation

• Reasons for studying concepts of programming

languages
• Increased capacity to express idea.

• Improved background for choosing appropriate languages.

• Increased ability to learn new language.

• Better understanding of the significance of implementation.

• Overall advancement of computing.

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Programming Domains

• Scientific Applications
• Floating point arithmetic, Arrays and Matrices, loops and selection

• FORTRAN, ALGOL 60

• Business Applications
• Reports, Decimal numbers and Character data, Decimal arithmetic

• COBOL

• Artificial Intelligence
• Symbolic Computation mainly with names
• Linked list
• LISP, Prolog

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Programming Domains

• System Programming
• Deals with low level features

• UNIX

• Scripting Languages
• Initially began with collection of commands in a file and then followed by control
statements , functions etc.

• Java Script, PHP

• Special Purpose Languages

• Produce business reports, Instruct programmable Machine tools, System
Simulation.

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PM as a Course
• What is not
– Do not teach you a programming language
– Do not teach you how to program

• What is
– Introduce fundamental concepts of programming languages
– Discuss design issues of various language constructs
– Examine design/implementation choices for these constructs
– Compare design alternatives

• Need to be familiar in at least one PL

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Course Logistics
Grading Policy

20%

Quiz
50% Midsem Examination
EndSem Examination
30%

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Reference Books

Ravi Sethi, "Programming Languages: Concepts and Constructs" 2nd Edition by

Addison Wesley.

Robert W. Sebesta, "Concepts of Programming Languages", 10th Edition by

Pearson Publishers.

Aho, Lam, Sethi and Ullman, "Compilers Principles, Techniques, and Tools".
Pearson Education. Low Price Edition. 2004

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Course Outline
• Overview and motivation
• Imperative Programming:
 Describing Syntax and Semantics
 Names, Bindings, and Scopes
 Data Types
 Expressions and Assignment
 Control Structures
 Subprograms
• Object Oriented Programming: Abstract Data Types, Encapsulation,
Information Hiding.
• Functional and Logic Programming Languages

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Course summary
• Principles are emphasized more than details.
• Methods are emphasized more than results.
• Semantics is emphasized more than syntax.

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What is a programming language?
A language that is intended for the expression of computer
programs and that is capable of expressing any computer
program.

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Programming Language
A programming language is an artificial language designed
to express computations or algorithms that can be
performed by a computer
A program is computer coding of
an algorithm that
– Takes input Input
– Performs some calculations on
the input
Program
– Generates output

Output
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Who Needs Programming
Languages?
• Computers' native tongue is machine language

• Programmers need higher level languages, because:

– They can't write machine language correctly
– They can't read machine language fluently
– They can't express their ideas in machine language
efficiently
– Life is too short to program in machine language.

• A formal language is not only a man-machine interface

but also a person-to-person language!

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Von Neumann Architecture

• Stored program concept

• Data and instruction have same format

• Interpret sequences as data and instructions

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The von Neumann
Architecture

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Imperative Lang. & von
Neumann Architecture
• Imperative languages, most dominant, because of von
Neumann computers
• Data and programs stored in memory
• Memory is separate from CPU
• Instructions and data are piped from memory to CPU
• Basis for imperative languages
• Variables model memory cells
• Assignment statements model piping
• Iteration is efficient

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The von Neumann Architecture

• Fetch-execute-cycle (on a von Neumann architecture

computer)

initialize the program counter

repeat forever
fetch the instruction pointed by the counter
increment the counter
decode the instruction
execute the instruction
end repeat

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Language Categories

• Imperative
• Central features are variables, assignment statements, and
iteration
• Include languages that support object-oriented programming
• Include scripting languages
• Include the visual languages
• Examples: C, Java, Perl, JavaScript, Visual BASIC, .NET, C++

• Functional
• Main means of making computations is by applying functions to
given parameters
• Examples: LISP, Scheme

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Language Categories

• Logic
• Rule-based (rules are specified in no particular order)
• Example: Prolog

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Programming Paradigms
Imperative – action oriented, sequence of actions.
Functional - symbolic data processing.
Object-Oriented - classes of objects.
Logic - logic reasoning.

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Imperative Programming

• General purpose imperative languages: Fortran, Pascal and C.

• Fortran: Familiar notations and efficiency, machine language programs
are more efficient as they do not require translation.
• Language of choice for scientific programming: used mathematical
notations, machine independent.
• Algol 60 was developed as a common language to share programs and
describe numerical processes- widely admired language.
• Pascal was designed as a teaching language, similar to Algol 60 in
syntactic constructs with minor differences.
• C was developed as an implementation language for software for UNIX
OS. C provides a rich set of operators, terse syntax and efficient access
to the machine.

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Functional Programming
• LISP (list processor): language designed for applications in artificial intelligence.
• Example of a List with 3 elements of which the third is a sublist-
(Shakespeare wrote (the tempest))
• Designed primarily for symbolic data processing.
• It has been used for symbolic calculations in differential and integral calculus,
mathematical logic, game playing, etc.
• Scheme: version of LISP popular for teaching and research due to its clean
design.
• Common LISP: is an advancement over proliferations of LISP i.e. MacLISP/
InterLISP. MacLISP emphasized performance and production quality while
InterLISP introduced the notion of a programming environment with a structured
editor tied to the syntax.
• CLOS is an object oriented extension, Common Lisp Object System.
• Other functional languages: ISWIM (not practically implemented), ML, Miranda,
Haskell

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Object oriented Programming

• Key concept of OO Programming: class of objects, classification of

objects into classes and subclasses (generalization and
specialization).
• Simula: Designed as both a programming language and a
description language.
• C++ and Smalltalk: popular OO languages, descendant of Simula by
taking the notion of objects and classes.
• C++ is an advancement over C which adds object oriented features
to imperative programming in C.
• Smalltalk was designed as part of personal computing environment,
it is an interactive system with a graphical user interface.

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Logic Programming

• Prolog: was developed for natural language processing.

• It uses a specialized form of logical reasoning to answer
queries.
• Used for a variety of applications from databases to
expert systems.
• Prolog programs have the expressiveness of logic.

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A short history of programming
Languages
1950s : LISP, FORTRAN
1960s : AlGOL 60, ISWIM, Simula
1970s : C, Pascal, Prolog, Smalltalk
During 1970 : a lot of PLs were designed.
1980s : C++, ML, Miranda, Haskell, CommonLISP

– Numerically based languages. (FORTRAN,ALGOL)

– Business languages. (COBOL)
– Artificial intelligence languages. (LISP, Prolog)
– Systems languages. ( C)

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A short history of programming languages
(cont.)

50s and 60s :

– Early high level languages : FORTRAN, COBOL,
ALGOL60
– Early functional languages : LISP
– Next leap forward: Algol68, SIMULA67, BASIC
70s:
– High level and structured programming: Pascal
– Systems programming: C, modula-2
– Logical programming: Prolog
– Improvement of functional programming: Scheme

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A short history of programming languages
(cont.)

80s:
– Development of functional programming:
CommonLisp, ML, Haskell, Miranda
– Need for reliability and maintainability: Ada
– Object-oriented programming: Smalltalk, C++
90s:
– Fourth-generation languages
– Visual languages : Delphi
– Scripting languages : Perl

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What Makes a Good PL?
Language evaluation criteria:
Readability: the ease with which programs can be read
and understood
Writability: the ease with which a language can be used to
create programs
Reliability: a program performs to its specifications under
all conditions
Cost

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Language Evaluation Criteria

CRITERIA

Characteristic READABILITY WRITABILITY RELIABILITY

Simplicity X X X
Orthogonality X X X
Data types X X X
Syntax design X X X
Support for abstraction X X
Expressivity X X
Type checking X
Exception handling X
Restricted aliasing X

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Features Related to Readability
Overall simplicity: language is more readable if
– Fewer features and basic constructs
Readability problems occur whenever program’s author uses a subset different
from that familiar to reader
– Fewer feature multiplicity (i.e., doing the same operation with different ways)
– Minimal operator overloading

Orthogonality
– A relatively small set of primitive constructs can be combined in a relatively small
number of ways
– The combination is legal.
– Too much orthogonality can also cause problems

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Features Related to Readability
Control statements
– Sufficient control statements for structured prog.
 can read program from top to bottom w/o jump

Data types and structures

– Adequate facilities for defining data type & structure

Syntax considerations
– Identifier or keywords
– Special words and methods of forming compound statements
– Form and meaning: self-descriptive constructs, meaningful keywords

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Writability
Simplicity and orthogonality
– But, too orthogonal may cause errors undetected

Support for abstraction

– Ability to define and use complex structures or operations in ways that allow
details to be ignored
– Abstraction in process (e.g. subprogram), data

Expressivity
– A set of relatively convenient ways of specifying operations
– Example: the inclusion of for statement in many modern languages

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Reliability
Type checking
– Testing for type errors, e.g. subprogram parameters

Exception handling
– Intercept run-time errors & take corrective measures

Aliasing
– Presence of two or more distinct references for the same memory location

Readability and writability

– A language that does not support “natural” ways of expressing an algorithm will
necessarily use “unnatural” approaches, and hence reduced reliability

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Cost
Training programmers to use language
Writing programs (closeness to particular applications)
Compiling programs
Executing programs: run-time type checking
Language implementation system: availability of free
compilers
Reliability: poor reliability leads to high costs
Maintaining programs

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Language Design Trade-Offs
Reliability vs. cost of execution
– e.g., Java demands all references to array elements be checked for proper
indexing but that leads to increased execution costs

Readability vs. writability

– e.g., use of many powerful operators (and a large number of new symbols),
allowing complex computations to be written in a compact program but at the
cost of poor readability

Writability (flexibility) vs. reliability

– e.g., C++ pointers are powerful and very flexible but not reliably used

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Language Implementation
Compiler - source code translation into machine code (all at
once)
Interpreter - machine is brought up to the language (one
statement at a time)

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Implementation Methods
• Compilation
• Programs are translated into machine language.

• Pure Interpretation
• Programs are interpreted by another program known
as an interpreter.

• Hybrid Implementation Systems

• A compromise between compilers and pure
interpreters.

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Layered View of Computer

The operating system and

Language implementation are
layered over machine interface
of a computer

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The Compilation
Process

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Compilation

• Translate high-level program (source language) into

machine code (machine language).

• Compilation process has several phases:

• lexical analysis: converts characters in the source program into lexical units e.g.:
identifiers / keywords, operators, punctuation…
• syntax analysis: transforms lexical units into parse trees which represent the
syntactic structure of program.
• Intermediate code generation & Semantics analysis: generate intermediate code
and does type checking.
• Code optimization: (optional)
• code generation: machine code is generated.

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Additional Compilation
Terminologies
• Load module (executable image):
• the user and system code together.

• Linking and loading:

• the process of collecting system program units and
linking them to a user program.
• As well as linking other user programs to it.

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Compiled C

Source Linker
compiler .o
code and
files
in C loader

Machine
code (exe)

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Pure Interpretation
• No translation
Program interpreted by another program (interpreter) without translation
– Interpreter acts a simulator or virtual machine
– Machine is brought to the level of language by building higher level
machine called an interpreter that can run the language directly.
• Easier implementation of programs (run-time errors can easily and
immediately be displayed)

• Slower execution (10 to 100 times slower than compiled programs)

Decoding of higher level language programs is more complex, decoding has
to be done every time a statement is executed.
• Often requires more space
• Used in APL, SNOBOL, LISP.
• Now rare for traditional high-level languages
• Significant comeback with some Web scripting languages (e.g., JavaScript,
PHP)
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Pure Interpretation Process

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Comparisons
• Interpretation is slower as decoding of higher level language programs is
more complex, decoding has to be done every time a statement is executed.
• Compiled languages have bias towards static properties since all compiling
decisions are made at translation time. Interpreted languages can deal with
dynamic properties.
• Interpretation is more flexible: due to direct running on the source code,
interpreter can allow to add features or correct errors in the source code.

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Hybrid Implementation Systems

• A compromise between compilers and pure interpreters.

• A high-level language program is translated to an

intermediate language that allows easy interpretation.

• Faster than pure interpretation since the source

language statements are decoded only once.
• Examples
• Perl programs are partially compiled to detect errors before interpretation.
• Initial implementations of Java were hybrid;
• the intermediate form, byte code, provides portability to any machine that has a
byte code interpreter and a run-time system (together, these are called Java
Virtual Machine).

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Hybrid
Implementation
Process

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Just-in-Time Implementation
Systems
• Initially translate programs to an intermediate language.

• Then compile the intermediate language of the

subprograms into machine code when they are called
Machine code version is kept for subsequent calls.

• JIT systems are widely used for Java programs.

• .NET languages are implemented with a JIT system.

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Summary

• The study of programming languages is valuable for a number of reasons:

• Increase our capacity to use different constructs
• Enable us to choose languages more intelligently
• Makes learning new languages easier

• Most important criteria for evaluating programming languages include:

• Readability, writability, reliability, cost

• Major influences on language design have been

• machine architecture and software development methodologies

• The major methods of implementing programming languages are:

• compilation, pure interpretation, and hybrid implementation

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