Concurrent test generation using concolic multi-trace analysis

Niloofar Razavi, Franjo Ivančić, Vineet Kahlon, Aarti Gupta

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

8 Scopus citations

Abstract

Discovering concurrency bugs is inherently hard due to the nondeterminism in multi-thread scheduling. Predictive analysis techniques have been successfully used to find such bugs by observing given test runs, and then searching for other interesting thread interleavings. For sequential code, concolic execution techniques have been used successfully to generate interesting test inputs to increase structural code coverage such as branch or statement coverage. In this paper, we propose the use of a concolic multi-trace analysis (CMTA) to efficiently increase code coverage in concurrent programs. We have implemented CMTA, and show encouraging results for some interesting benchmark programs.

Original languageEnglish (US)
Title of host publicationProgramming Languages and Systems - 10th Asian Symposium, APLAS 2012, Proceedings
Pages239-255
Number of pages17
DOIs
StatePublished - Dec 1 2012
Externally publishedYes
Event10th Asian Symposium on Programming Languages and Systems, APLAS 2012 - Kyoto, Japan
Duration: Dec 11 2012Dec 13 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7705 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other10th Asian Symposium on Programming Languages and Systems, APLAS 2012
CountryJapan
CityKyoto
Period12/11/1212/13/12

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Razavi, N., Ivančić, F., Kahlon, V., & Gupta, A. (2012). Concurrent test generation using concolic multi-trace analysis. In Programming Languages and Systems - 10th Asian Symposium, APLAS 2012, Proceedings (pp. 239-255). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7705 LNCS). https://doi.org/10.1007/978-3-642-35182-2_17