Using automatically generated invariants for regression testing and bug localization

Parth Sagdeo, Nicholas Ewalt, Debjit Pal, Shobha Vasudevan

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

Abstract

We present Preambl, an approach that applies automatically generated invariants to regression testing and bug localization. Our invariant generation methodology is Precis, an automatic and scalable engine that uses program predicates to guide clustering of dynamically obtained path information. In this paper, we apply it for regression testing and for capturing program predicates information to guide statistical analysis based bug localization. We present a technique to localize bugs in paths of variable lengths. We are able to map the localized post-deployment bugs on a path to pre-release invariants generated along that path. Our experimental results demonstrate the efficacy of the use of PRECIS for regression testing, as well as the ability of Preambl to zone in on relevant segments of program paths.

Original languageEnglish (US)
Title of host publication2013 28th IEEE/ACM International Conference on Automated Software Engineering, ASE 2013 - Proceedings
Pages634-639
Number of pages6
DOIs
StatePublished - 2013
Event2013 28th IEEE/ACM International Conference on Automated Software Engineering, ASE 2013 - Palo Alto, CA, United States
Duration: Nov 11 2013Nov 15 2013

Publication series

Name2013 28th IEEE/ACM International Conference on Automated Software Engineering, ASE 2013 - Proceedings

Other

Other2013 28th IEEE/ACM International Conference on Automated Software Engineering, ASE 2013
Country/TerritoryUnited States
CityPalo Alto, CA
Period11/11/1311/15/13

ASJC Scopus subject areas

  • Software

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