Localization of linearizability faults on the coarse-grained level

Zhenya Zhang, Peng Wu, Yu Zhang

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

2 Citations (Scopus)

Abstract

Linearizability is an important correctness criterion that guarantees the safety of concurrent data structures. Due to the nondeterminism of concurrent executions, reproduction and localization of a linearizability fault still remain challenging. The existing work mainly focuses on model checking the thread schedule space of a concurrent program on a fine-grained (state) level, and hence suffers from the severe problem of state space explosion. This paper presents a tool called CGVT to build a small test case that is sufficient enough for reproducing a linearizability fault. Given a possibly long history that has been detected non-linearizable, CGVT first locates the operations causing a linearizability violation, and then synthesizes a short test case for further investigation. Moreover, we present several optimization techniques to improve the effectiveness and efficiency of CGVT. We have applied CGVT to 10 concurrent objects, while the linearizability of some of the concurrent objects is unknown yet. The experiments show that CGVT is powerful and efficient enough to build the test cases adaptable for a fine-grained analysis.

Original languageEnglish
Title of host publicationProceedings - SEKE 2017
Subtitle of host publication29th International Conference on Software Engineering and Knowledge Engineering
PublisherKnowledge Systems Institute Graduate School
Pages272-277
Number of pages6
ISBN (Electronic)1891706411
DOIs
Publication statusPublished - 2017
Externally publishedYes
Event29th International Conference on Software Engineering and Knowledge Engineering, SEKE 2017 - Pittsburgh, United States
Duration: Jul 5 2017Jul 7 2017

Publication series

NameProceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE
ISSN (Print)2325-9000
ISSN (Electronic)2325-9086

Conference

Conference29th International Conference on Software Engineering and Knowledge Engineering, SEKE 2017
Country/TerritoryUnited States
CityPittsburgh
Period7/5/177/7/17

All Science Journal Classification (ASJC) codes

  • Software

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