Automatic construction of an effective training set for prioritizing static analysis warnings

Guangtai Liang, Ling Wu, Qian Wu, Qianxiang Wang, Tao Xie, Hong Mei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In order to improve ineffective warning prioritization of static analysis tools, various approaches have been proposed to compute a ranking score for each warning. In these approaches, an effec-tive training set is vital in exploring which factors impact the ranking score and how. While manual approaches to build a training set can achieve high effectiveness but suffer from low efficiency (i.e., high cost), existing automatic approaches suffer from low effectiveness. In this paper, we propose an automatic approach for constructing an effective training set. In our approach, we select three categories of impact factors as input attributes of the training set, and propose a new heuristic for identifying actionable warnings to automatically label the training set. Our empirical evaluations show that the precision of the top 22 warnings for Lucene, 20 for ANT, and 6 for Spring can achieve 100% with the help of our constructed training set.

Original languageEnglish (US)
Title of host publicationASE'10 - Proceedings of the IEEE/ACM International Conference on Automated Software Engineering
Pages93-102
Number of pages10
DOIs
StatePublished - 2010
Externally publishedYes
Event25th IEEE/ACM International Conference on Automated Software Engineering, ASE'10 - Antwerp, Belgium
Duration: Sep 20 2010Sep 24 2010

Publication series

NameASE'10 - Proceedings of the IEEE/ACM International Conference on Automated Software Engineering

Other

Other25th IEEE/ACM International Conference on Automated Software Engineering, ASE'10
Country/TerritoryBelgium
CityAntwerp
Period9/20/109/24/10

Keywords

  • Generic-bug-related lines
  • Static analysis tools
  • Training-set construction
  • Warning prioritization

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Human-Computer Interaction
  • Software

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