STRUCTURE OF PROGRAMMING LANGUAGES

1. Dr Yasser Fouad STRUCTURE OF PROGRAMMING LANGUAGES

2. Book

3. Quote of the Day “A language that doesn't affect the way you think about programming, is not worth knowing.” - Alan Perlis

4. You work in a little web search company Your boss says: “We will conquer the world only if our search box answers all questions the user may ask.” So you build gcalc: CS212 helps you answer questions your boss really cares about.

5. Then you decide to get a PhD You get tired of the PowerPoint and its animations. You embed a domain-specific language (DSL) into Ruby. …

6. Reasons for Studying Concepts of Programming Languages • Increased ability to express ideas • Improved background for choosing appropriate languages • Increased ability to learn new languages • Better understanding of significance of implementation • Overall advancement of computing

7. How is this class different? It’s about: • foundations of programming langauges • but also how to design your own languages • how to implement them • and about PL tools, such as analyzers • Ans also learn about some classical C.S. algorithms.

8. Why a developer needs PL New languages will keep coming • Understand them, choose the right one. Write code that writes code • Be the wizard, not the typist. Develop your own language. • Are you kidding? No. Learn about compilers and interpreters. • Programmer’s main tools.

9. Overview • how many languages does one need? • how many languages did you use? Let’s list them here:

10. Develop your own language Are you kidding? No. Guess who developed: • PHP • Ruby • JavaScript • perl Done by smart hackers like you • in a garage • not in academic ivory tower Our goal: learn good academic lessons • so that your future languages avoid known mistakes

11. Programming Domains • Scientific applications • Large number of floating point computations • Fortran • Business applications • Produce reports, use decimal numbers and characters • COBOL • Artificial intelligence • Symbols rather than numbers manipulated • LISP • Systems programming • Need efficiency because of continuous use • C • Web Software • Eclectic collection of languages: markup (e.g., XHTML), scripting (e.g., PHP), general-purpose (e.g., Java)

12. Programming Methodologies Influences • 1950s and early 1960s: Simple applications; worry about machine efficiency • Late 1960s: People efficiency became important; readability, better control structures • structured programming • top-down design and step-wise refinement • Late 1970s: Process-oriented to data-oriented • data abstraction • Middle 1980s: Object-oriented programming • Data abstraction + inheritance + polymorphism

14. Language Categories • Imperative • Central features are variables, assignment statements, and iteration • Examples: C, Pascal • Functional • Main means of making computations is by applying functions to given parameters • Examples: LISP, Scheme • Logic • Rule-based (rules are specified in no particular order) • Example: Prolog • Object-oriented • Data abstraction, inheritance, late binding • Examples: Java, C++ • Markup • New; not a programming per se, but used to specify the layout of information in Web documents • Examples: XHTML, XML

15. Program • A program is a machine-compatible representation of an algorithm • If no algorithm exists for performing a task, then the task can not be performed by a machine • Programs and algorithms they represent collectively referred to as Software

16. Favorite programming language June 2012 • Python (3,054) • Ruby (1,723) • JavaScript (1,415) • C (970) • C# (829) • PHP (666) • Java (551) • C++ (529) • Haskell (519) • Clojure (459) • CoffeeScript (362) • Objective C (326) • Lisp (322) • Perl (311) • Scala (233) • Scheme (190) • Other (188) • Erlang (162) • Lua (145) • SQL (101)

18. job listings collected from Dice.com • Python 3,456 (+32.87%) • Ruby 2,141 (+39.03%) • HTML5 (+276.85%) • Flash 1,261 (+95.2%) • Silverlight 865 (-11.91%) • COBOL 656 (-10.75%) • Assembler 209 (-1.42%) • PowerBuilder (-18.71%) • FORTRAN 45 (-33.82%) • Java 17,599 (+8.96%) • XML 10,780 (+11.70%) • JavaScript (+11.64%) • HTML 9,587 (-1.53%) • C# 9,293 (+17.04%) • C++ 6,439 (+7.55%) • AJAX 5,142 (+15.81%) • Perl 5,107 (+3.21%) • PHP 3,717 (+23%)

20. Languages in Common Use

21. ENIAC (1946, University of Philadelphia) ENIAC program for external ballistic equations:

22. Programming the ENIAC

23. ENIAC (1946, University of Philadelphia) • programming done by • rewiring the interconnections • to set up desired formulas, etc • Problem (what’s the tedious part?) • programming = rewiring • slow, error-prone • solution: • store the program in memory! • birth of von Neuman paradigm

24. Assembly – the language (UNIVAC 1, 1950) Idea: mnemonic (assembly) code • Then translate it to machine code by hand (no compiler yet) • write programs with mnemonic codes (add, sub), with symbolic labels, • then assign addresses by hand Example of symbolic assembler clear-and-add a add b store c translate it by hand to something like this (understood by CPU) B100 A200 C300

25. Assembly Language ADDI R4 R2 21 ADDI R4,R2,21 10101100100000100000000000010101 • Use symbols instead of binary digits to describe fields of instructions. • Every aspect of machine visible in program: • One statement per machine instruction. • Register allocation, call stack, etc. must be managed explicitly. • No structure: everything looks the same.

26. Assembler – the compiler (Manchester, 1952) • a loop example, in MIPS, a modern-day assembly code: loop: addi $t3, $t0, -8 addi $t4, $t0, -4 lw $t1, theArray($t3) # Gets the last lw $t2, theArray($t4) # two elements add $t5, $t1, $t2 # Adds them together... sw $t5, theArray($t0) # ...and stores the result addi $t0, $t0, 4 # Moves to next "element“ # of theArray blt $t0, 160, loop # If not past the end of # theArray, repeat jr $ra

27. High-level Language • Provides notation to describe problem solving strategies rather than organize data and instructions at machine-level. • Improves programmer productivity by supporting features to abstract/reuse code, and to improve reliability/robustness of programs. • Requires a compiler.

28. FORTRAN I (1954-57) Langauge, and the first compiler • Produced code almost as good as hand-written • Huge impact on computer science • Modern compilers preserve its outlines By 1958, >50% of all software is in FORTRAN

29. FORTRAN I Example: nested loops in FORTRAN • a big improvement over assembler, • but annoying artifacts of assembly remain: • labels and rather explicit jumps (CONTINUE) • lexical columns: the statement must start in column 7 • The MIPS loop from previous slide, in FORTRAN: DO 10 I = 2, 40 A[I] = A[I-1] + A[I-2] 10 CONTINUE

30. Side note: designing a good language is hard Good language protects against bugs, but lessons take a while. An example that caused a failure of a NASA planetary probe: buggy line: DO 15 I = 1.100 what was intended (a dot had replaced the comma): DO 15 I = 1,100 because Fortran ignores spaces, compiler read this as: DO15I = 1.100 which is an assignment into a variable DO15I, not a loop. This mistake is harder to make (if at all possible) with the modern lexical rules (white space not ignored) and loop syntax for (i=1; i < 100; i++) { … }

31. Goto considered harmful L1: statement if expression goto L1 statement Dijkstra says: gotos are harmful • use structured programming • lose some performance, gain a lot of readability how do you rewrite the above code into structured form?

32. Evolution of Programming Languages • ALGOL - 60 (ALGOrithmic Language) Goals : Communicating Algorithms Features : Block Structure (Top-down design) Recursion (Problem-solving strategy) BNF - Specification • LISP (LISt Processing) • Goals : Manipulating symbolic information • Features : List Primitives • Interpreters / Environment

33. Evolution of Programming Languages • Pascal • Goal : Structured Programming, Type checking, • Compiler writing. • Features : • Rich set of data types for efficient • algorithm design • E.g., Records, sets, ... • Variety of “readable” single-entry • single-exit control structures • E.g., for-loop, while-loop,... • Efficient Implementation • Recursive descent parsing

34. Other Languages • Functional • LISP, Scheme • ML, Haskell • Logic • Prolog • Object-oriented • Smalltalk, SIMULA, Modula-3, Oberon • C++, Java, C#, Eiffel, Ada-95 • Hybrid • Python, Ruby, Scala • Application specific languages and tools

35. Programming Languages 3/4 • C • Bell labs  Dennis Ritchie, 1973 • C++ • Bjarne Stroustrup, 1980 • Hybrid OOP • Java • Sun Microsystems (formally announced in May 1995) • Pure OOP • Web programming

36. Designed for gluing applications : flexibility Interpreted Dynamic typing and variable creation Data and code integrated : meta-programming supported Examples: PERL, Tcl, Python, Ruby, PHP, Scheme, Visual Basic, Scala, etc. Designed for building applications : efficiency Compiled Static typing and variable declaration Data and code separated : cannot create/run code on the fly Examples: PL/1, Ada, Java, C, C++, C#, Scala, etc. Scripting vs Systems Programming Languages

37. Current Trend • Multiparadigm languages • Functional constructs for programming in the small • Focus on conciseness and correctness • Object-Oriented constructs for programming in the large • Focus on programmer productivity and code evolution • Example languages • Older: Python, Ruby, • Recent: Scala, F#, etc

38. Scheme (dialect of LISP) • Recursive definitions • Symbolic computation : List Processing • Higher-order functions • Dynamic type checking • Functional + Imperative features • Automatic storage management • Provides a uniform executable platform for studying, specifying, and comparing languages.

39. Java vs Scala //Java - what we're used to seeing public String buildEpochKey(String... keys) { StringBuilder s = new StringBuilder("elem") for(String key:keys) { if(key != null) { s.append(".") s.append(key) } } return s.toString(). toLowerCase() }

40. Java vs Scala //Scala def buildEpochKey(keys: String*): String = { ("elem" +: keys) filter(_ != null) mkString(".") toLowerCase }

41. 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

42. Compilation • Translate high-level program (source language) into machine code (machine language) • Slow translation, fast execution • Compilation process has several phases: • lexical analysis: converts characters in the source program into lexical units • syntax analysis: transforms lexical units into parse trees which represent the syntactic structure of program • Semantics analysis: generate intermediate code • code generation: machine code is generated

43. The Compilation Process

44. Additional Compilation Terminologies • Load module (executable image): the user and system code together • Linking and loading: the process of collecting system program and linking them to user program

45. Pure Interpretation • No translation • Easier implementation of programs (run-time errors can easily and immediately displayed) • Slower execution (10 to 100 times slower than compiled programs) • Often requires more space • Becoming rare on high-level languages • Significant comeback with some Web scripting languages (e.g., JavaScript)

46. 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 • 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)

47. Just-in-Time Implementation Systems • Initially translate programs to an intermediate language • Then compile intermediate language into machine code • Machine code version is kept for subsequent calls • JIT systems are widely used for Java programs • .NET languages are implemented with a JIT system

48. Programming Environments • The collection of tools used in software development • UNIX • An older operating system and tool collection • Nowadays often used through a GUI (e.g., CDE, KDE, or GNOME) that run on top of UNIX • Borland JBuilder • An integrated development environment for Java • Microsoft Visual Studio.NET • A large, complex visual environment • Used to program in C#, Visual BASIC.NET, Jscript, J#, or C++

49. What Does This C Statement Mean? *p++ = q++ modifies *p increments p increments q Does this mean… … or … or *p = *q; ++p; ++q; tp = p; ++p; tq = q; ++q; *tp = *tq; *p = *q; ++q; ++p;

50. Languages • Simula • Smalltalk • Algol • Cobol • F# • Prolog • Pascal • Modula-2 • ADA • PL/I • CORBA • PERL • BASIC • JAVASCRIPT • LISP • MIRANDA • ML • SCHEMA • SNOBOL • APL • DELPHI • MAYA