SimRT: An automated framework to support regression testing for data races

Tingting Yu, Witawas Srisa-An, Gregg Rothermel

Research output: Contribution to journalConference articlepeer-review

36 Scopus citations

Abstract

Concurrent programs are prone to various classes of difficult-to-detect faults, of which data races are particularly prevalent. Prior work has attempted to increase the cost-effectiveness of approaches for testing for data races by employing race detection techniques, but to date, no work has considered cost-effective approaches for re-testing for races as programs evolve. In this paper we present SimRT, an automated regression testing framework for use in detecting races introduced by code modifications. SimRT employs a regression test selection technique, focused on sets of program elements related to race detection, to reduce the number of test cases that must be run on a changed program to detect races that occur due to code modifications, and it employs a test case prioritization technique to improve the rate at which such races are detected. Our empirical study of SimRT reveals that it is more efficient and effective for revealing races than other approaches, and that its constituent test selection and prioritization components each contribute to its performance.

Original languageEnglish
Pages (from-to)48-59
Number of pages12
JournalProceedings - International Conference on Software Engineering
Issue number1
DOIs
StatePublished - May 31 2014
Event36th International Conference on Software Engineering, ICSE 2014 - Hyderabad, India
Duration: May 31 2014Jun 7 2014

Bibliographical note

Funding Information:
This work has been supported in part by the Air Force Office of Scientific Research through award FA9550-10-1-0406 and the Army Research Office through award W911NF-13-1-0154.

Publisher Copyright:
© 2014 ACM.

Keywords

  • Concurrency
  • Data Races
  • Kernels
  • Processes
  • Testing

ASJC Scopus subject areas

  • Software

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