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Z-Ranking: Using Statistical Analysis to Counter the Impact of Static Analysis Approximations

  • Ted Kremenek
  • Dawson Engler
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2694)

Abstract

This paper explores z-ranking, a technique to rank error reports emitted by static program checking analysis tools. Such tools often use approximate analysis schemes, leading to false error reports. These reports can easily render the error checker useless by hiding real errors amidst the false, and by potentially causing the tool to be discarded as irrelevant. Empirically, all tools that effectively find errors have false positive rates that can easily reach 30–100%. Z-ranking employs a simple statistical model to rank those error messages most likely to be true errors over those that are least likely. This paper demonstrates that z-ranking applies to a range of program checking problems and that it performs up to an order of magnitude better than randomized ranking. Further, it has transformed previously unusable analysis tools into effective program error finders.

Keywords

Error Report True Error Ranking Scheme Weak Hypothesis Real Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Ted Kremenek
    • 1
  • Dawson Engler
    • 1
  1. 1.Computer Systems LaboratoryStanford UniversityStanfordUSA

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