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29 Cards in this Set
- Front
- Back
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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 • Better use of languages that are already known • Overall advancement of computing |
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Programming Domains |
Scientific applications – Large numbers of floating point computations; use of arrays – Fortran • Business applications – Produce reports, use decimal numbers and characters – COBOL • Artificial intelligence – Symbols rather than numbers manipulated; use of linked lists – LISP • Systems programming – Need efficiency because of continuous use – C • Web Software – Eclectic collection of languages: markup (e.g., HTML), scripting (e.g., PHP), general-purpose (e.g., Java) |
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Language Evaluation Criteria |
Readability: the ease with which programs can be read and understood • Writability: the ease with which a language can be used to create programs • Reliability: conformance to specifications (i.e., performs to its specifications) • Cost: the ultimate total cost |
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Readability |
Overall simplicity – A manageable set of features and constructs – Minimal feature multiplicity – Minimal operator overloading • Orthogonality – A relatively small set of primitive constructs can be combined in a relatively small number of ways – Every possible combination is legal and meaningful • Data types – Adequate predefined data types • Syntax considerations – Identifier forms: flexible composition – Special words and methods of forming compound statements – Form and meaning: self-descriptive constructs, meaningful keywords |
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Writability |
• Simplicity and orthogonality – Few constructs, a small number of primitives, a small set of rules for combining them • Support for abstraction – The ability to define and use complex structures or operations in ways that allow details to be ignored – Two categories of abstraction: process (subprogram example) and data (binary tree example in text) • Expressivity – A set of relatively convenient ways of specifying operations – Strength and number of operators and predefined functions |
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Reliability |
Type checking – Testing for type errors – compile- or run-time; Ex. int vs. float • Exception handling – Intercept run-time errors and take corrective measures – In Ada, C++, Java, C# (not C, Fortran) • Aliasing – Presence of two or more distinct referencing methods for the same memory location – A dangerous feature in programming languages • Readability and writability – A language that does not support “natural” ways of expressing an algorithm will require the use of “unnatural” approaches, and hence reduced reliability |
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Cost |
Training programmers to use the language • Writing programs (closeness to particular applications) • Compiling programs • Executing programs • Language implementation system: availability of free compilers • Reliability: poor reliability leads to high costs • Maintaining programs |
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Others |
Portability – The ease with which programs can be moved from one implementation to another – Influenced by the standardization of the language • Generality – The applicability to a wide range of applications • Well-definedness – The completeness and precision of the language’s official definition |
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Influences on Language Design |
Computer Architecture – Languages are developed around the prevalent computer architecture, known as the von Neumann architecture • Program Design Methodologies – New software development methodologies (e.g., object-oriented software development) led to new programming paradigms and by extension, new programming languages |
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Computer Architecture Influence |
Well-known computer architecture: Von Neumann • Imperative languages, most dominant, because of von Neumann computers – Data and programs stored in memory – Memory is separate from CPU – Instructions and data are piped from memory to CPU – Basis for imperative languages • Variables model memory cells • Assignment statements model piping • Iteration is efficient |
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The von Neumann Architecture |
• Fetch-execute-cycle (on a von Neumann architecture computer) initialize the program counter repeat forever fetch the instruction pointed by the counter increment the counter decode the instruction execute the instruction end repeat |
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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 |
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Language Categories |
Imperative • Functional • Logic • Markup/programming hybrid |
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Imperative |
– Central features are variables, assignment statements, and iteration – Include languages that support object-oriented programming – Include scripting languages – Include the visual languages – Examples: C, Java, Perl, JavaScript, Visual BASIC .NET, C++ |
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Functional |
– Main means of making computations is by applying functions to given parameters – Examples: LISP, Scheme, ML, F# |
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• Logic |
– Rule-based (rules are specified in no particular order) – Example: Prolog |
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• Markup/programming hybrid |
– Markup languages extended to support some programming – Examples: JSTL, XSLT |
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Language Design Trade-Offs |
Reliability vs. cost of execution – Example: Java demands all references to array elements be checked for proper indexing, which leads to increased execution costs • Readability vs. writability Example: APL provides many powerful operators (and a large number of new symbols), allowing complex computations to be written in a compact program but at the cost of poor readability • Writability (flexibility) vs. reliability – Example: C++ pointers are powerful and very flexible but are unreliable |
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Implementation Methods |
Compilation – Programs are translated into machine language; includes JIT systems – Use: Large commercial applications • Pure Interpretation – Programs are interpreted by another program known as an interpreter – Use: Small programs or when efficiency is not an issue • Hybrid Implementation Systems – A compromise between compilers and pure interpreters – Use: Small and medium systems when efficiency is not the first concern |
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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 |
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Load module (executable image): |
the user and system code together |
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Linking and loading: |
the process of collecting system program units and linking them to a user program |
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Von Neumann Bottleneck |
Connection speed between a computer’s memory and its processor determines the speed of a computer • Program instructions often can be executed much faster than the speed of the connection; the connection speed thus results in a bottleneck • Known as the von Neumann bottleneck; it is the primary limiting factor in the speed of computers |
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Pure Interpretation |
• No translation • Easier implementation of programs (run-time errors can easily and immediately be displayed) • Slower execution (10 to 100 times slower than compiled programs)
• Often requires more space • Now rare for traditional high-level languages • Significant comeback with some Web scripting languages (e.g., JavaScript, PHP) |
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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) |
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Just-in-Time Implementation Systems |
Initially translate programs to an intermediate language • Then compile the intermediate language of the subprograms into machine code when they are called • Machine code version is kept for subsequent calls • JIT systems are widely used for Java programs • .NET languages are implemented with a JIT system • In essence, JIT systems are delayed compilers |
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Preprocessors |
Preprocessor macros (instructions) are commonly used to specify that code from another file is to be included • A preprocessor processes a program immediately before the program is compiled to expand embedded preprocessor macros • A well-known example: C preprocessor – expands #include, #define, and similar macros |
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Programming Environments |
A 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 runs on top of UNIX • Microsoft Visual Studio.NET – A large, complex visual environment • Used to build Web applications and non-Web applications in any .NET language • NetBeans – Related to Visual Studio .NET, except for applications in Java |
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Summary of chapter 1 |
The study of programming languages is valuable for a number of reasons: – Increase our capacity to use different constructs – Enable us to choose languages more intelligently – Makes learning new languages easier • Most important criteria for evaluating programming languages include: – Readability, writability, reliability, cost • Major influences on language design have been machine architecture and software development methodologies • The major methods of implementing programming languages are: compilation, pure interpretation, and hybrid implementation |
