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EDA Software Engineering

EDA Software Engineering

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EDA Software Engineering

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  1. Outline SE Methods Software Reuse Software Libraries Rapid Prototyping Tools Computer-Aided Programming Goal Understand SE ideas relevant to CAD Learn pointers to specific SE tools EDA Software Engineering

  2. Software Reuse Software Libraries Rapid Prototyping Tools Computer-Aided Programming Software Engineering Methods

  3. The fastest way to code is don't • use existing software - programs, modules, classes, functions, data structures - "previous work" analogy • sources - friends, project, group, system, bboards, servers, vendors - pieces of source code, packages, libraries Bigger != better • software value is in functionality and performance • no one cares how many lines of code are in your software • 20,000 lines of code != PhD • software size is only useful as a complexity metric for planning Software Reuse

  4. Reduced implementation time • Hitachi "software factory" produces 1500 lines/person/month of debugged code • Only 500 lines/person/month are new. Rest is reused. • 20,000/1500 = 13 months to implement thesis software! - debugged too! Higher quality • can put more effort into a module that is reused many times • do it right once and it is right everywhere Easier maintenance • less to maintain • easier to understand Benefits of Software Reuse

  5. Difficult to Understand • no comments, documentation, explanatory variable/function names • example: variables foo, bar, baz, count1, count2,... Specialized • customized to platform, data structures, application • example: sort routine only for positive integers Not Modular • spread across many locations in existing application • example: structure initialized in file1, modified in file2,... Result: often less time and error to start from scratch Barriers to Software Reuse

  6. Understandable • useful comments - not "cnt++; /* increment counter */" • complete man page - painful, but necessary • meaningful names - "rectangle", "IncCounter",... General • clean interface - streams, sequences, etc. • specialization via procedure argument, derived type, etc. • example: qsort uses procedure arg to compare two array elements Modular • use good practices - structured programming, information hiding, abstract types, object-oriented programming • everything in one bundle • example: include MakeRectangle, DeleteRectangle in module Writing Reusable Software

  7. Good fundamentals • algorithms • data structures • modularity Simple interface • character stream, list, array - example: stream interface of UNIX filters • functions with few arguments - example: functions to build up argument structure for operation Avoid premature efficiency • do not sacrifice generality for efficiency • identify and eliminate program bottlenecks later Cleanliness is Next to Godliness

  8. Example BeginPolygon(); MoveTo(0, 0); LineTo(1, 0); LineTo(1, 1); LineTo(0, 1); RectPoly = EndPolygon(); ... UnionPoly = Union(RectPoly, DiamondPoly); Books • Software Tools, Kernighan, Plauger • Writing Efficient Programs, Bentley Cleanliness

  9. Library == bundled abstract/abstract-like data types • implementation often unknown General • C library - system interface, misc. • C Programmer's Toolbox - allocation, lists, trees, hash tables in C • Code Farms, Inc. - collections, trees, lists, database,... in C, C++ • NIH - collections, numbers, dictionary, geometry, vectors,... in C++ • GNU - I/O, strings, numbers, RNG, collections, vectors,... in C++ Graphics • Motif • X library (Xlib) • X toolkit (Xt) • InterViews • Tk Software Libraries

  10. Math • Numerical Recipes in C • matrix packages - LINPACK, LAPACK, EISPACK,... Geometry • 2-D polygon package - C • Spatial Technologies ACIS solid modeler - C++ classes Specialized • XF interface builder for Tk/Tcl • BOS object set for Tcl Somewhere out there is a library for almost everything Software Libraries

  11. Goals • rapid! - <10% of time to do traditional C/C++ implementation • functionality - implement functions to permit experimentation or use • okay performance - system only needs to be fast enough to try it out or be acceptable • easily modified - for iteration during experimentation, or for maintainability What is Rapid Prototyping? 1: Quick assembly of a partial or complete system for experimentation prior to full requirements, specification, and implementation. 2: Quick assembly of a tool or system for temporary or permanent use, using special-purpose languages and existing tools.

  12. General-Purpose Languages • Bourne shell (sh), C shell (csh), Korn shell (ksh), born-again shell (bash), tcsh • LISP? Pattern Languages • awk, gawk • sed, grep • perl Extension Languages • Tk/Tcl • Emacs LISP Parser Generators • lex, yacc UNIX utilities • comm, diff, ed, find, sort, uniq,... Existing Tools Rapid Prototyping Tools

  13. They exist! • lot of needed functionality already built in Quick edit-compile-debug loop • interpreted, compile-on-fly, pre-compiled Simple I/O • mostly just ASCII text, not binary • frequently stream I/O - easier interfacing Easily controlled from other programs • command line interface, extension language, streams, files,... Often used in combination • e.g. awk scripts in a shell script Prototyping Tool Characteristics

  14. Command Interpreters • programming language access to UNIX commands • sh, csh are "standard" and portable • shell to use matter of personal taste Applications • need general control constructs • file testing, directory access • need functionality of many UNIX tools • "central control" of application Performance • UNIX commands normally dominates runtime Shell Languages

  15. Domain • scan text input, do computation, pass result to the output Key Ideas • regular expression matching • state machine transformations Performance • Stream/file I/O dominates application • CPU efficiency is less important Pattern Languages

  16. Language • set of <pattern, action> pairs • for each input line, execute actions of matching patterns • patterns are regular expressions, conditionals, etc. • actions are expressions, statements • gawk is extended version of awk Examples • {print $2} - print the second field of every line • length > 72 {i++} - count # lines >72 characters long END {print i} Applications • good for simple computations on input stream • e.g. take average of a column of numbers Awk

  17. Language • stream editor • commands are [address [,address]] function [args] • apply commands with matching addresses to each input line • can read and write files, test, branch, use buffer space, etc. Examples • sed -e 's/^ //' file - delete first three spaces of each line • sed -e 'r foo' file - place contents of foo between each line Applications • good for local transformations on text streams • e.g. capitalize the first word at the beginning of each sentence • most common use is regular expression query-replace Sed

  18. Language • regular expression pattern search • print all lines that do/do not match pattern • grep - limited RE, egrep - full RE, fgrep - fixed strings Example • grep '^[abc]h' file - print all lines beginning with "ah", "bh", or "ch" • grep -v 'Defect' file - print all lines except those with "Defect" • grep -n foo * - print all lines (w/filename and line number) of all files with "foo" Applications • general tool for simple text file searches • egrep usually fastest and most general Grep

  19. Language • practical extraction and report language • combination of awk, sed, sh, csh, etc., functionality • C-like syntax, operators, and semantics • faster, more powerful Features • fast text, binary scan • command line switch parsing • math, system, file testing, I/O control, IPC functions • subroutines • awk (a2p), sed (s2p), find (find2perl) to perl translators Applications • facilities management - more secure than C programs • networking applications • general-purpose • for simple things, use sed, awk,... Perl

  20. Archie • program to access databases of useful stuff available on Internet C version • 7889 lines of C code and scripts • 1822 lines for VMS support • 230 lines for MS DOS support Perl version • 1146 lines of Perl code and scripts • runs on any platform supporting Perl The Perl version is more reliable and about as fast Perl Example

  21. Tcl • general-purpose • small and efficient • easily linked into applications Emacs LISP • write programs to process text • programs to interact with other processes - shell windows • "live inside Emacs" - mail, bboards, compiling • advantages: can watch things happen, record keystrokes, can run in batch, familiar • disadvantages: startup time (0.2s on DS5000/25), size (912K) Extension Languages

  22. Lex, Yacc (Flex, Bison) • generate lexical analyzer and parser with C callbacks Applications • parse interchange language of some complexity • e.g. CIF, Stream, MIF • very simple format, easier to use pattern or programming language • if speed not critical, use for final application Books • Principles of Compiler Design, Aho, Ullman, Seth (the "Dragon" book) • even though parsing concepts are complicated, learning and using is still faster than using C Parser Generators

  23. Think of them as special-purpose tools for program construction • use combination of not-quite-ideal tools rather than write new one Examples • How many unique words are in a word list? #!/bin/csh -f sort $1 | uniq | wc | awk '{print $1}' • Change files 'foo.dat' to 'foo.d' #!/bin/csh -f foreach i (`ls *.dat`) mv $i {$i:r}.d end UNIX Utilities

  24. Use your suite of programs as a toolbox • follow "tool building" and software reuse practices • see Software Tools book for examples Identify common needs in a multi-person project • parser for common language • display routines • special-purpose Tk widgets Build on your past, recycle, don't dispose Existing Tools

  25. When all else fails, you are forced to do some coding Computer aids for coding • improved blocking and tackling, i.e. basics • speed up edit-compile-debug loop • code management • code analysis Note: coding != programming • you already have algorithms, program structure Computer-Aided Programming

  26. Edit • make source code know to system • write with editor, copy, etc. Compile • transform source to runnable form • incorporate debugging instrumentation • not necessarily with compiler Debug • run code on test inputs • use instruments to isolate and identify errors in source Good programmers loop several times developing code by iterative refinement Edit-Compile-Debug Loop

  27. Program Build • keep track of what is necessary to build program • source modules • libraries • module dependencies • e.g. make Version Control • maintain multiple versions of code • diffs, patches to code • current and old versions • log files of changes • access control for multiple users • e.g. rcs, sccs Code Management

  28. Navigation • cross-reference - who calls who • class hierarchy Static Checking • portability rules - e.g. architecture/OS dependencies • code checking - syntax, semantics, style - e.g. lint Performance Analysis • post-processing of profiling information Code Analysis

  29. UNIX Tools • cc, make, dbx,... Emacs • LISP commands • interface to UNIX tools Commercial Products • Centerline ViewCenter • HP Softbench • Parc Place Systems ObjectWorks/C++ • Centerline CodeCenter and ObjectCenter • etc., etc. CA Programming Environments

  30. Compilers • cc, gcc - C compilers • CC, g++ - C++ compiler (currently a C translator) Source Debuggers • dbx, gdb - language independent Code Management • make - compilation control • ar, ranlib - archive and library generation • rcs, sccs - source code control • makedepend - X11 dependencies Code Analysis • lint - C source checker • cxref - C cross-reference • prof, gprof - run-time profiling UNIX Tools

  31. Link to UNIX tools • fork make • step through compile errors in source code • e.g. GNU Emacs Compile package Language Sensitive Editor • formatting - indentation, pretty-printing,... • syntax-directed editing - paren balancing, delete expression,... • e.g. C Mode in GNU Emacs Implemented with LISP extension language • user can customize behavior Emacs

  32. • Your first reaction to any new programming task should not be to start writing C or C++ • Look around for existing modules or libraries • Use rapid prototyping tools if possible • As last resort, write code, but use computer-aided programming tools to do it quickly Conclusions