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Safety as a Software Metric

Safety as a Software Metric. Matthias Felleisen and Robert Corky Cartwright Rice University. Why Safety as a Metric? . Measuring Software: Syntax versus Semantics What is Programming Language Safety ? What Makes an Individual Program Safe ? How about Teaching Program Safety? .

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Safety as a Software Metric

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  1. Safety as a Software Metric Matthias Felleisen and Robert Corky Cartwright Rice University

  2. Why Safety as a Metric? • Measuring Software: Syntax versus Semantics • What is Programming LanguageSafety ? • What Makes an Individual Program Safe ? • How about Teaching Program Safety?

  3. Why Measure Software? • correct and efficient software • maintainable software • extensible software

  4. What do Metrics Measure? • lines of code • number of procedures, gotos, loops, modules, statements versus expressions, … • in short: Syntactic Attributes of software

  5. What should Metrics Measure? • correctness • extensibility • maintainability • in short: semantic and organizational attributes

  6. Measuring Correctness is Difficult • … • goal: measure certain aspects of correctness • specifically: assume the programming language is safe, what kind of problems can we predict?

  7. Safe Programming Languages

  8. Safety -- A High-Level View (1) “Close the valve by 10 degrees!” “Turned the valve by 10 degrees!”

  9. Safety -- A High-Level View (2) “Close the valve by 10 degrees!” “Turned the valve by 15 degrees!”

  10. Safety -- A High-Level View (3) “OUCH!” “Close the valve by 10 degrees!”

  11. Safety -- A High-Level View (4) ------------- ------- ------------- --------- ----------

  12. Safety -- A High-Level View (5) ------------- ------- ------------- --------- ---------- ERROR!

  13. C and C++ are NOT Safe! int f(int n, int m) { int r = n % m; if (0 == r) return m; else return f(m,r); } main() { char a = 'a'; char b = 'b'; int mn[2] = {24,6}; char c = 'c'; char d = 'd'; printf("%d\n",f(mn[0],mn[1])); printf("%d\n",f(mn[0],c)); printf("%d\n",f(mn[0],mn[2])); }

  14. Safety in Programming Languages • a safe language protects every computational primitive, e.g., +, *, if, vector-lookup, record dereference, … • protection is implemented with a mixture of compile-time and run-time checks • safety guarantees errors are caught • safety greatly increases effectiveness of debugging

  15. Safety • … is NOT just TYPE checking!

  16. Examples Fortran C C++ Perl ML Eiffel Java Scheme (untyped, but safe) SAFE Languages UNSAFE Languages

  17. Safe Programs and Measuring Safety

  18. Measuring the Safety of Programs • programs in safe languages signal errors • programs should not signal errors • determine whether any computational primitive might signal an error • make programmers explain potential faults

  19. MrSpidey: Measuring the Safety of Scheme Programs • Scheme is a dialect of Algol and LISP • lexical scope, first-class functions (“mini-objects”) • LISP’s syntax (parentheses) and primitives (cons, car, and cdr)

  20. some function call, somewhere in the program

  21. SYMBOLS are bad for +

  22. general input shapes

  23. Measuring Safety is More than Checking Types • check general “data shapes” • lists with at least N items • vector references • …

  24. list with at least one NUMBER

  25. NIL is not okay

  26. An Elaborate Example from the Scheme Front-end S-expression S-expression (let (<var> <rhs:exp>) <body:exp>) ((lambda (<var>) <body:exp>) <rhs:exp>)

  27. … yields many checks weak invariant

  28. stronger invariant yields stronger results

  29. Teaching with Safety Metrics

  30. Program Construction: Rice University, Fall 1998 • course on program safety • understanding • measuring • based on Scheme and Java

  31. On Safety of Languages and Programs • programming language safety • program safety • theory and tools for “measuring” program safety • logics that conservatively approximate semantics • logics that extend the logic of type checking

  32. The Pragmatics of MrSpidey • using MrSpidey: • checking • understanding potential fault sites: • data set • data flow • is it a problem with the program? • is it a problem with the theory/tool? • if the latter, can a re-organization help?

  33. Hands-on Work • homework assignments • sets of problems for each bullet • increasing complexity • theory and practice • project: implement sequential subset of Java • modules and data invariants that cross boundaries • exploring large pieces of code

  34. Evaluation (1) • course evaluation: excellent • targeted questions: • understanding of language safety • understanding of program safety • understanding of measuring safety with theorem provers • effectiveness of homeworks versus project

  35. Evaluation (2) Positives: appreciate safety appreciate tools appreciate theory understand the above based on homework Negatives project too large

  36. Summary • new, semantics-based thinking about “metrics” • extensions: measuring stronger invariants (numeric constraints, polyvariant); measuring organization (patterns?) • teaching: a good approach to have students understand partial correctness

  37. Thank You Mike Fagan (92) Andrew Wright (94) Cormac Flanagan (96) Matthew Flatt Shriram Krishnamurthi Robby Findler

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