CS 242 Thanks! Review � Teaching Assistants • Mike Cammarano • TJ Giuli • Hendra Tjahayadi John Mitchell � Graders • Andrew Adams Tait Larson • Kenny Lau Aman Naimat Final Exam • Vishal Patel Justin Pettit Wednesday Dec 8 • and more … 8:30 – 11:30 AM Gates B01, B03 Course Goals There are many programming languages � Understand how programming languages work � Early languages • Fortran, Cobol, APL, ... � Appreciate trade-offs in language design � Algol family � Be familiar with basic concepts so you can understand discussions about • Algol 60, Algol 68, Pascal, …, PL/1, … Clu, Ada, Modula, Cedar/Mesa, ... • Language features you haven’t used � Functional languages • Analysis and environment tools • Lisp, FP, SASL, ML, Miranda, Haskell, Scheme, Setl, ... • Implementation costs and program efficiency � Object-oriented languages • Language support for program development • Smalltalk, Self, Cecil, … • Modula-3, Eiffel, Sather, … • C++, Objective C, …. Java General Themes in this Course � Language provides an abstract view of machine � Concurrent languages • We don’t see registers, length of instruction, etc. • Actors, Occam, ... � The right language can make a problem easy; • Pai-Lisp, … wrong language can make a problem hard � Proprietary and special purpose languages • Could have said a lot more about this • TCL, Applescript, Telescript, ... � Language design is full of difficult trade-offs • Postscript, Latex, RTF, … • Expressiveness vs efficiency, ... • Domain-specific language • Important to decide what the language is for � Specification languages • Every feature requires implementation data structures and algorithms • CORBA IDL, ... • Z, VDM, LOTOS, VHDL, … 1
A good language design presents abstract Good languages designed with specific goals (often an intended application) machine, an idealized view of computer • C: systems programming • Lisp: cons cells, read-eval-print loop • Lisp: symbolic computation, automated reasoning • FP: ?? • FP: functional programming, algebraic laws • ML: functions are basic control structure, memory model includes closures and reference cells • ML: theorem proving • C: the underlying machine + abstractions • Clu, ML modules: modular programming • Simula: activation records and stack; object references • Simula: simulation • Smalltalk: objects and methods • Smalltalk: Dynabook, • C++: ?? • C++: add objects to C • Java: Java virtual machine • Java: set-top box, internet programming Design Issues � Language design involves many trade-offs � In general, high-level languages/features are: • space vs. time • slower than lower-level languages – C slower than assembly • efficiency vs. safety – C++ slower than C • efficiency vs. flexibility – Java slower than C++ • efficiency vs. portability • provide for programs that would be • static detection of type errors vs. flexibility difficult/impossible otherwise • simplicity vs. "expressiveness" etc – Microsoft Word in assembly language? � These must be resolved in a manner that is – Extensible virtual environment without objects? – Concurrency control without semaphores or monitors? • consistent with the language design goals • preserves the integrity of abstract machine Many program properties are undecidable Languages are still evolving (can't determine statically ) • Halting problem • Object systems • nil pointer detection • Adoption of garbage collection • alias detection • Concurrency primitives; abstract view of concurrent systems • perfect garbage detection • Domain-specific languages • etc. • Network programming Static type systems • Aspect-oriented programming and many other “fads” • detect (some) program errors statically – Every good idea is a fad until is sticks • can support more efficient implementations • are less flexible than either no type system or a dynamic one 2
Summary of the course Lisp Summary � Lisp, 1960 � Modularity and objects � Successful language • encapsulation • Symbolic computation, experimental programming � Fundamentals • dynamic lookup � Specific language ideas • lambda calculus • subtyping • Expression-oriented: functions and recursion • denotational semantics • inheritance • Lists as basic data structures • functional prog � Simula and Smalltalk • Programs as data, with universal function eval � ML and type systems • Stack implementation of recursion via "public � C++ � Block structure and pushdown list" activation records � Java • Idea of garbage collection. � Exceptions and � Concurrency continuations Fundamentals Algol Family and ML � Grammars, parsing � Evolution of Algol family • Recursive functions and parameter passing � Lambda calculus • Evolution of types and data structuring � Denotational semantics � ML: Combination of Lisp and Algol-like features � Functional vs. Imperative Programming • Expression-oriented • Is implicit parallelism a good idea? • Higher-order functions • Is implicit anything a good idea? • Garbage collection • Abstract data types • Module system • Exceptions Types and Type Checking Type inference algorithm � Types are important in modern languages � Example • Program organization and documentation - fun f(x) = 2+x; Graph for λ x. ((plus 2) x) • Prevent program errors > val it = fn : int → int t → int = int → int • Provide important information to compiler λ � How does this work? � Type inference int (t = int) @ Assign types to leaves • Determine best type for an expression, based on known information about symbols in the expression int → int @ Propagate to internal x : t � Polymorphism nodes and generate + 2 : int • Single algorithm (function) can have many types constraints int → int → int � Overloading real → real → real Solve by substitution • Symbol with multiple meanings, resolved at compile time 3
Block structure and storage mgmt Summary of scope issues � Block-structured languages and stack storage � Block-structured lang uses stack of activ records • Activation records contain parameters, local vars, … � In-line Blocks • Also pointers to enclosing scope • activation records � Several different parameter passing mechanisms • storage for local, global variables � First-order functions � Tail calls may be optimized • parameter passing � Function parameters/results require closures • tail recursion and iteration • Closure environment pointer used on function call � Higher-order functions • Stack deallocation may fail if function returned from call • deviations from stack discipline • Closures not needed if functions not in nested blocks • language expressiveness => implementation complexity Control Modularity and Data Abstraction � Structured Programming � Step-wise refinement and modularity • Go to considered harmful • History of software design � Exceptions � Language support for information hiding • “structured” jumps that may return a value • Abstract data types • dynamic scoping of exception handler • Datatype induction � Continuations • Packages and modules � Generic abstractions • Function representing the rest of the program • Generalized form of tail recursion • Datatypes and modules with type parameters • Design of STL Concepts in OO programming Simula 67 � Four main language ideas � First object-oriented language • Encapsulation � Designed for simulation • Dynamic lookup • Later recognized as general-purpose prog language • Subtyping � Extension of Algol 60 • Inheritance � Standardized as Simula (no “67”) in 1977 � Why OOP ? � Inspiration to many later designers • Extensible abstractions; separate interface from impl • Smalltalk � Compare oo to conventional (non-oo) lang • C++ • Can represent encapsulation and dynamic lookup • ... • Need inheritance and subtyping as basic constructs 4
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