brief intro for Programming Languages 02-chapter-1
- 1. CS 415: Programming CS 415: Programming Languages Languages Chapter 1 Chapter 1 Aaron Bloomfield Aaron Bloomfield Fall 2005 Fall 2005
- 2. The first computers The first computers Scales – computed relative weight of two items Scales – computed relative weight of two items Computed if the first item’s weight was less than, equal to, or Computed if the first item’s weight was less than, equal to, or greater than the second item’s weight greater than the second item’s weight Abacus – performed mathematical computations Abacus – performed mathematical computations Primarily thought of as Chinese, but also Japanese, Mayan, Primarily thought of as Chinese, but also Japanese, Mayan, Russian, and Roman versions Russian, and Roman versions Can do square roots and cube roots Can do square roots and cube roots
- 4. Computer Size Computer Size ENIAC then… ENIAC today… With computers (small) size does matter! With computers (small) size does matter!
- 5. Why study programming Why study programming languages? languages? Become a better software engineer Become a better software engineer Understand how to use language features Understand how to use language features Appreciate implementation issues Appreciate implementation issues Better background for language selection Better background for language selection Familiar with range of languages Familiar with range of languages Understand issues / advantages / disadvantages Understand issues / advantages / disadvantages Better able to learn languages Better able to learn languages You might need to know a lot You might need to know a lot
- 6. Why study programming Why study programming languages? languages? Better understanding of implementation issues Better understanding of implementation issues How is “this feature” implemented? How is “this feature” implemented? Why does “this part” run so slowly? Why does “this part” run so slowly? Better able to design languages Better able to design languages Those who ignore history are bound to repeat it… Those who ignore history are bound to repeat it…
- 7. Why are there so many Why are there so many programming languages? programming languages? There are thousands! There are thousands! Evolution Evolution Structured languages -> OO programming Structured languages -> OO programming Special purposes Special purposes Lisp for symbols; Snobol for strings; C for systems; Lisp for symbols; Snobol for strings; C for systems; Prolog for relationships Prolog for relationships Personal preference Personal preference Programmers have their own personal tastes Programmers have their own personal tastes Expressive power Expressive power Some features allow you to express your ideas better Some features allow you to express your ideas better
- 8. Why are there so many Why are there so many programming languages? programming languages? Easy to use Easy to use Especially for teaching / learning tasks Especially for teaching / learning tasks Ease of implementation Ease of implementation Easy to write a compiler / interpreter for Easy to write a compiler / interpreter for Good compilers Good compilers Fortran in the 50’s and 60’s Fortran in the 50’s and 60’s Economics, patronage Economics, patronage Cobol and Ada, for example Cobol and Ada, for example
- 9. Programming domains Programming domains Scientific applications Scientific applications Using the computer as a large calculator Using the computer as a large calculator Fortran and friends, some Algol, APL Fortran and friends, some Algol, APL Using the computer for symbol manipulation Using the computer for symbol manipulation Mathematica Mathematica Business applications Business applications Data processing and business procedures Data processing and business procedures Cobol, some PL/1, RPG, spreadsheets Cobol, some PL/1, RPG, spreadsheets Systems programming Systems programming Building operating systems and utilities Building operating systems and utilities C, PL/S, ESPOL, Bliss, some Algol and derivitaves C, PL/S, ESPOL, Bliss, some Algol and derivitaves
- 10. Programming domains Programming domains Parallel programming Parallel programming Parallel and distributed systems Parallel and distributed systems Ada, CSP, Modula, DP, Mentat/Legion Ada, CSP, Modula, DP, Mentat/Legion Artificial intelligence Artificial intelligence Uses symbolic rather than numeric computations Uses symbolic rather than numeric computations Lists as main data structure Lists as main data structure Flexibility (code = data) Flexibility (code = data) Lisp in 1959, Prolog in the 1970s Lisp in 1959, Prolog in the 1970s Scripting languages Scripting languages A list of commands to be executed A list of commands to be executed UNIX shell programming, awk, tcl, Perl UNIX shell programming, awk, tcl, Perl
- 11. Programming domains Programming domains Education Education Languages designed to facilitate teaching Languages designed to facilitate teaching Pascal, BASIC, Logo Pascal, BASIC, Logo Special purpose Special purpose Other than the above… Other than the above… Simulation Simulation Specialized equipment control Specialized equipment control String processing String processing Visual languages Visual languages
- 12. Programming paradigms Programming paradigms You have already seen assembly language You have already seen assembly language We will study five language paradigms: We will study five language paradigms: Top-down (Algol 60 and Fortran) Top-down (Algol 60 and Fortran) Functional (Scheme and/or OCaml) Functional (Scheme and/or OCaml) Logic (Prolog) Logic (Prolog) Object oriented (Smalltalk) Object oriented (Smalltalk) Aspect oriented (AspectJ) Aspect oriented (AspectJ)
- 13. Programming language history Programming language history Pseudocodes (195X) – Many Pseudocodes (195X) – Many Fortran (195X) – IBM, Backus Fortran (195X) – IBM, Backus Lisp (196x) – McCarthy Lisp (196x) – McCarthy Algol (1958) – Committee (led to Pascal, Ada) Algol (1958) – Committee (led to Pascal, Ada) Cobol (196X) – Hopper Cobol (196X) – Hopper Functional programming – FP, Scheme, Haskell, ML Functional programming – FP, Scheme, Haskell, ML Logic programming – Prolog Logic programming – Prolog Object oriented programming – Smalltalk, C++, Python, Object oriented programming – Smalltalk, C++, Python, Java Java Aspect oriented programming – AspectJ, AspectC++ Aspect oriented programming – AspectJ, AspectC++ Parallel / non-deterministic programming Parallel / non-deterministic programming
- 14. Compilation vs. Translation Compilation vs. Translation Translation: does a ‘mechanical’ translation of the source Translation: does a ‘mechanical’ translation of the source code code No deep analysis of the syntax/semantics of the code No deep analysis of the syntax/semantics of the code Compilation: does a thorough understanding and Compilation: does a thorough understanding and translation of the code translation of the code A compiler/translator changes a program from one A compiler/translator changes a program from one language into another language into another C compiler: from C into assembly C compiler: from C into assembly An assembler then translates it into machine language An assembler then translates it into machine language Java compiler: from Java code to Java bytecode Java compiler: from Java code to Java bytecode The Java interpreter then runs the bytecode The Java interpreter then runs the bytecode
- 15. Compilation stages Compilation stages Scanner Scanner Parser Parser Semantic analysis Semantic analysis Intermediate code generation Intermediate code generation Machine-independent code improvement (optional) Machine-independent code improvement (optional) Target code generation Target code generation Machine-specific code improvement (optional) Machine-specific code improvement (optional) For many compilers, the result is assembly For many compilers, the result is assembly Which then has to be run through an assembler Which then has to be run through an assembler These stages are machine-independent! These stages are machine-independent! The generate “intermediate code” The generate “intermediate code”
- 16. Compilation: Scanner Compilation: Scanner Recognizes the ‘tokens’ of a program Recognizes the ‘tokens’ of a program Example tokens: ( 75 main int { return ; foo Example tokens: ( 75 main int { return ; foo Lexical errors are detected here Lexical errors are detected here More on this in a future lecture More on this in a future lecture
- 17. Compilation: Parser Compilation: Parser Puts the tokens together into a pattern Puts the tokens together into a pattern void main ( int argc , char ** argv ) { void main ( int argc , char ** argv ) { This line has 11 tokens This line has 11 tokens It is the beginning of a method It is the beginning of a method Syntatic errors are detected here Syntatic errors are detected here When the tokens are not in the correct order: When the tokens are not in the correct order: int int foo ; int int foo ; This line has 4 tokens This line has 4 tokens After the type (int), the parser expects a variable After the type (int), the parser expects a variable name name Not another type Not another type
- 18. Compilation: Semantic analysis Compilation: Semantic analysis Checks for semantic correctness Checks for semantic correctness A semantic error: A semantic error: foo = 5; foo = 5; int foo; int foo; In C (and most languages), a variable has to be In C (and most languages), a variable has to be declared before it is used declared before it is used Note that this is syntactically correct Note that this is syntactically correct As both lines are valid lines as far as the parser is concerned As both lines are valid lines as far as the parser is concerned
- 19. Compilation: Intermediate code Compilation: Intermediate code generation (and improvement) generation (and improvement) Almost all compilers generate intermediate code Almost all compilers generate intermediate code This allows part of the compiler to be machine- This allows part of the compiler to be machine- independent independent That code can then be optimized That code can then be optimized Optimize for speed, memory usage, or program Optimize for speed, memory usage, or program footprint footprint
- 20. Compilation: Target code Compilation: Target code generation (and improvement) generation (and improvement) The intermediate code is then translated into the The intermediate code is then translated into the target code target code For most compilers, the target code is assembly For most compilers, the target code is assembly For Java, the target code is Java bytecode For Java, the target code is Java bytecode That code can then be further optimized That code can then be further optimized Optimize for speed, memory usage, or program Optimize for speed, memory usage, or program footprint footprint