1 Introduction Why Study Programming Languages? Choosing the right - PowerPoint PPT Presentation

1 Introduction Why Study Programming Languages? • Choosing the right language for the job • Designing a better language • Languages we know determine how we think about programming “A language that doesn’t affect the way you think about programming is not worth knowing.” — Alan Perlis 1

8 Languages in an hour We look briefly at some of the variety we find in programming languages Just to show variety; don’t worry if you don’t understand most of the programs • Forth • Fortran • Eiffel • Cobol • Bison • Lisp • Mercury • APL 2

1.1 Fortran • ( FOR mula TRAN slator) • Designed in the mid 1950s • Subroutines, but no recursion or nesting • Control flow by goto, conditional, and bounded iteration • Commonly used in engineering and science applications • Fortran 90 and 95 are much improved versions, compared to the original versions; work is under way on Fortran 2000 3

Fortran example integer I, MX, MN,A(100) real RS read(A(I), I = 1, 100) MX = A(1) MN = A(1) do 10 I = 2, 100 if (A(I).gt.MX) MX = A(I) if (A(I).lt.MN) MN = A(I) 10 continue RS = (MN + MX)/2 write RS end 4

1.2 Cobol • ( CO mmon B usiness O riented L anguage) • Designed around 1959 • For processing large amounts of data • Verbose, for readability • Powerful notion of file • Supports goto, conditional goto, for loop • Program consists of 4 divisions: Identification, Environment, Data, and Procedure • Still commonly used for business applications 5

Cobol example (simplified) data division file section FD STFILE 01 STUDENT 02 STUDENT-NAME picture A(15) 02 COURSE occurs 30 times 03 COURSE-NAME picture AAAA999 03 SCORE picture 99 02 STUDENT-ID picture 99999 working-storage section 01 TOTAL picture 999999 6

Cobol example (simplified) (2) procedure division init. open input STFILE. move zero to TOTAL. sum. read STFILE; at end go to fin. perform adding varying J from 1 by 1 until J > 30. go to sum. adding. read COURSE(J). add SCORE to TOTAL. fin. display TOTAL. close STFILE. 7

1.3 Lisp (LISt Processing) • Designed in the late 1950s, inspired by the lambda calculus. • Still in active use, with several dialects surviving • Functional, that is, mainly based on function application • Functions are first-class objects, even if higher-order and/or recursive • Typeless: S-expression is the only type 8

Lisp (LISt Processing) (2) • S-expression is number, atom (symbol), or list d b c a This binary tree represents the S-expression (((a.nil).(b.c)).(d.nil)) 9

Lisp example (defun intersect (m n) (cond ((null m) nil) ((member (car m) n) (cons (car m) (intersect (cdr m) n))) (t (intersect (cdr m) n)))) This function returns the intersection of two lists 10

1.4 APL (A Programming Language) • Designed in the early 1960s • Extremely compact programs • Based on multidimensional arrays • Powerful array operations, elementwise, cumulative, . . . • Many special symbols, uses a special character set • Used in scientific and engineering applications 11

APL example A program that generates the first N Fibonacci numbers: ∇ FIB N [1] A ← 1 1 [2] → 2 × N > ρA ← A, + / − 2 ↑ A ∇ 12

APL example (2) A program that generates prime numbers up to N : (2 = + / [1]0 = S ◦ . | S ) /S ← ι N ) • With experience this becomes readable (?) • Some call APL a “write-only” language • Often easier to rewrite than modify a function • “concise” � = “readable!” 13

1.5 Forth • Designed in the early 1970s for use on small computers • Stack based: operations take their operands from the (single) stack and place their result(s) on the stack • No named parameters or local variables; data is handled by stack manipulation • Forth is the basis for the Postscript page description language 14

Forth example : sqr dup * ; n => n*n : dosum swap 1 + n, s => s, (n+1) swap over => (n+1), s, (n+1) sqr + ; => (n+1), (s+(n+1)^2) : sumsqr 0 swap n => 0, n 0 swap 0 => 0, 0, n, 0 do dosum loop => sum i=0 to n of i^2 15

1.6 Eiffel • Designed in the 1980s • Object oriented: definitions of operations and data are encapsulated together. • Uses inheritance to define new data structures and operations in terms of others • Design by contract: operations specify the initial conditions they require and the final conditions they will ensure • Polymorphic: can define operations and types that can work on objects of any type 16

Eiffel example class STACK[T] export push, pop, empty, full feature implementation: ARRAY[T] max_size: INTEGER nb_elements: INTEGER Create(n: INTEGER) is do if n>0 then max_size := n end; implementation.Create(1, max_size) end; 17

Eiffel example (2) empty: BOOLEAN is do Result := (nb_elements = 0) end; pop:T is require not empty do Result := implementation.entry(nb_elements); nb_elements := nb_elements - 1; ensure not full; nb_elements = old nb_elements - 1 end; ... 18

1.7 Bison • Originally developed in the 1980s as a free replacement for the older YACC language • Special purpose designed for writing parsers • Translates input into C source code • Usually used with a scanner generator such as FLEX • Usually used for only a small part of an application 19

Bison example %{ #define YYSTYPE double #include <math.h> %} %token NUM %left ’-’ ’+’ %left ’*’ ’/’ %left NEG /* negation--unary minus */ %right ’^’ /* exponentiation */ %% 20

Bison example (2) input: /* empty string */ | input line ; line: ’\n’ | exp ’\n’ { printf ("\t%.10g\n", $1); }; exp: NUM { $$ = $1; } | exp ’+’ exp { $$ = $1 + $3; } | exp ’-’ exp { $$ = $1 - $3; } | exp ’*’ exp { $$ = $1 * $3; } | exp ’/’ exp { $$ = $1 / $3; } | ’-’ exp %prec NEG { $$ = -$2; } | exp ’^’ exp { $$ = pow ($1, $3); } | ’(’ exp ’)’ { $$ = $2; } ; %% 21

1.8 Mercury • Originated in early 1990s at University of Melbourne • Logic/functional programming language • A program consists of clauses, that is, facts and rules that allow new facts to be deduced from old • Purely declarative • A program can be regarded as a knowledge-base (what to compute, rather than how) • Strong types and modes • Nondeterministic: some queries have multiple solutions • Control flow by backtracking as well as invocation • Parameter passing by unification (bidirectional) 22

Mercury example :- type list(T) ---> [] ; [T | list(T)]. :- pred append(list(T), list(T), list(T)). :- mode append(in, in, out) is det. :- mode append(in, out, in) is semidet. :- mode append(out, out, in) is multi. append([], C, C). append([A|B], C, [A|BC]) :- append(B, C, BC). 23

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2 Abstraction We all know that the only mental tool by means of which a very finite piece of reasoning can cover a myriad cases is called ”abstraction”; as a result the effective exploitation of his powers of abstraction must be regarded as one of the most vital activities of a competent programmer. . . . The purpose of abstracting is not to be vague, but to create a new semantic level in which one can be absolutely precise. — Edsgar Dijkstra 25

Abstraction (2) From the ultralingua.net dictionary: 1. The process of formulating general concepts by abstracting common properties of instances; generalization. 2. A general concept formed by extracting common features from specific examples. 26

Abstraction (3) • Good programmers continually look for better abstractions for what they are doing • Often find them in new functions or datatypes • Occasionally they can only be found in a different programming language or paradigm, or by using a language preprocessor • Once in a while, one must invent a new preprocessor or language or even paradigm 27

2.1 Abstraction in programming Some abstractions developed in computer science: • assembly language abstracted instruction numbers and formats • FORTRAN and other high-level languages abstracted the details of the actual machine • Operating Systems abstracted interaction with external entities • functions and procedures abstract a sequence of operations 28

2.1.1 Machine Language • Why do we have programming languages? • A (fragment of a) stored executable program ultimately looks like this: 00000010101111001010 00000010111111001000 00000011001110101000 and initially, this was what programmers wrote (or toggled into a computer’s front panel) • Each line is a command; command names, register numbers, etc , are encoded as numbers 29

2.1.2 Assembly Language • In assembly language the above might be written LOAD I ADD J STORE K “Take the value stored in I ’s cell, add the value stored in J ’s cell, and put the sum in K ’s cell” • I.e. , calculate K = I + J • Abstracts numeric opcodes to symbols, addresses to names 30

2.1.3 More intelligible languages • Historic trend towards more abstract, understandable programming notation • The language should help us write programs that are easy to read, easy to understand, easy to modify • This generally means higher levels of abstraction, and sometimes specialization for certain programming tasks • Different languages provide different abstractions; no one-size-fits-all language 31