Programming Languages WHY MORE? Wasn’t ONE ENOUGH? Introduction to CS260

Cart before the horse Before contrasting(text), get a feel for how languages are similar But as author does -> You need to think of programming on a different level You know how to implement algorithms Now learn to do it better In order to assess whether you have the best approach, you need to understand consequences of your choices

Does your approach take too long or too much memory? Is your approach easy to understand? Readable? Is it prone to errors Is your program reasonably extendable or have you programmed yourself into a corner? How can you possibly judge?

It’s a little early Your understanding of these concepts will become clearer with time and experience Understanding examples from other languages to illustrate a point is basically premature Start slowly First look for similarities in languages and then differences Develop a vocabulary for language issues

Similarities Learning new languages is not as difficult as it appears Many of the languages are similar to c/c++ This leverages you into a position of immediate knowledge of other languages Knowing how to categorize differences leads you to know lots of other information E.g. knowing the sex of a person leads you to be able to reach a number of other conclusions -> physical, emotional, etc

Major categories Representation of data Flow of control –expression –statement –procedure Interpreted vs compiled Scope of access to data and procedures Information hiding

Influence of language Try to think of any concept or idea without the use of words / language LANGUAGE shapes the way you think Greek language has a number of different words for different types of love. English tries to use the same word in different contexts. We struggle to differentiate the types of love. Programming language dictates our solution

Language influence Language User Interfaces OS Applications Architectural Features Methodologies design tcl create use create design utilize Provide access to

Applications are Interaction of Data Operations Language understanding begins by examining these areas!

Data Simple data types Aggregate data types User/Programmer extendable types System supplied and hardware supplied

Operations Expression level Statement level Procedural level Process level

Binding times Many of the features are characterized by when the feature can change E.g. consider allocation and initialization of a simple data type –compilation –beginning of execution –entry into procedure –entry into a code block

Scope How do you determine access to data –minimization is good Access to procedures –should you have access to all functions and all versions

Languages specialize Business -> COBOl Scientific -> FORTRAN Systems (OS) -> C General Purpose -> C/C++/Ada/java Internet -> java Scripting -> shell Artificial Intelligence -> Lisp/Prolog

Languages have tradeoffs Readability ( Simplicity ) vs Power ( Functionality ) Too little orthogonality (features dependent on other features - too many exceptions) vs too much orthogonality (independence creates too many possibilities and increased complexity) –c allows functions to return structs but not arrays (little) –allowing complicated expressions which evaluate to allocation on LHS of assignment is unreasonable e.g. (&c)+23=y+z

Syntax rules promoting readability may create programs more cumbersome to write –generally we accept the short-term inconvenience –Aside (syntax/form vs semantics/meaning) for statement Size of language vs cost of support tools Reliability vs cost/speed of execution Flexibility vs complexity/cost/safety Our most important concern is COST Many poor decisions are a result of not considering real costs!

Compiled vs interpreted Compiled –translate entire program into machine language –execute Interpret –translate piece –execute –repeat –more flexibility (e.g. changing data type)

Environments Development/testing environments influence our choices visual vs command line debugging aids compiler error assistance

Most important factors Machine architecture –von Neumann –in the end you have to implement what you design Methodology –modular –structured (top-down design) –object oriented

Time makes languages a melting pot of all good in other languages Java like c like pascal like algol like fortran … All embrace procedures/parameters/objects/etc Fundamentally different approaches become fewer