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Lecture 15 Semantic Analysis

Lecture 15 Semantic Analysis. KU | Fall 2019 | Drew Davidson. Announcements Lecture 15. P3 Code Out Tonight Lab 7 H6 posted a bit late Quiz 2 Review Session Review Session Sunday 5:00 – 7:00 Learned 1131 Pizza at 6:00. Live Assignments. H6. P3. Last Time Lecture 14 – Scope.

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Lecture 15 Semantic Analysis

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  1. Lecture 15 Semantic Analysis KU | Fall 2019 | Drew Davidson

  2. AnnouncementsLecture 15 P3 Code Out Tonight Lab 7 H6 posted a bit late Quiz 2 Review Session • Review Session Sunday • 5:00 – 7:00 • Learned 1131 • Pizza at 6:00 Live Assignments H6 P3

  3. Last TimeLecture 14 – Scope Semantics • Program meaning Scope • Name analysis Semantics

  4. Today’s OutlineLecture 15 – Semantic Analysis Error Checking • What counts as a bad program? • How do we detect bad programs? Limits of Analysis • The halting problem Semantics

  5. Error Checking Semantic Analysis Goal: save programmers from themselves • It’s not enough to compile the programmer’s code • Need to figure out what programmer meant to code

  6. Compiler As Mind ReaderSemantic Analysis

  7. A Compiler: Artist’s RenditionSemantic Analysis A grumpy old man that yells at you for breaking the rules

  8. Quick Audience PollLecture 15 – Semantic Analysis Does this C program compile? Should this C code compile? int a = 0; int main(){ if (false){ b = 6; } return a;}

  9. A Compiler’s Error-Checking ObligationLecture 15 – Semantic Analysis Understandability / Consistency

  10. The Compiler Before the CompilerSemantic Analysis

  11. Bug HuntingSemantic Analysis How do we prevent nonsense code from executing? • We’ll consider two ways of analysis: • Static • Dynamic Putting guardrails on computation

  12. Compiler PerspectiveStatic vs Dynamic Analysis Static • Code analysis without execution Dynamic • Code analysis through execution Checks done at compile time Analysis part of the compiler itself Checks done at run time Analysis embedded into the program

  13. Compiler Focus: Static AnalysisStatic vs Dynamic Analysis Doesn’t slow the program down • Ok to take longer • Ok to apply more heavyweight analysis Has a “holistic” view of the program • Has access to source code • Knowledge of non-executed program paths

  14. Limits of Error CheckingStatic Analysis We’d LOVE to ensure bug-free programs • Observe and report bugs before they are encountered Usually we can’t do this • Limits of static analysis

  15. Limits of Static AnalysisStatic Analysis Theoretical argument Practical argument

  16. The Halting ProblemStatic Analysis Does a computation ever terminate?

  17. Let’s Do Some VocabStatic Analysis Effective method: a procedure that is always yields a correct result on any input

  18. No Effective Method for HaltingStatic Analysis assume white_magic(Function p) returns true if p halts, false if p does not function black_magic(): if white_magic(black_magic): while true { }

  19. Ok, but that’s just Halting, right?Static Analysis

  20. Rice’s TheoremStatic Analysis - Limits of Error Checking “All non-trivial semantic properties of programs are undecidable”

  21. Rice’s Theorem - ImplicationsDetour – Limits of Error Checking • We’d like to perfectly capture all bugs • We can’t be right all of the time • We can choose HOW we are wrong

  22. Limits of Static AnalysisStatic Analysis Theoretical argument Practical argument

  23. Let’s do some Sciency-Sounding StuffEvaluation

  24. Evaluating a Bug DetectorEvaluation False True Positive Negative

  25. Soundness and CompletenessDetour – Limits of Error Checking All Programs All Programs Reported bugs Buggy Programs Buggy Programs Reported bugs Complete bug detection Sound bug detection

  26. Partial CorrectnessLimits of Error Checking • Make best-effort procedures that are neither sound nor complete • We can analyze the result of a statement under certain assumptions • Assume that the statement is executed • Assume that the statement actually completes

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