Locating fault-inducing patterns from structural inputs

Hai Feng Guo, Zongyan Qiu, Harvey Siy

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

1 Scopus citations


In this paper, we propose a new fault localization technique for testing software which requires structured input data. We adopt a symbolic grammar to represent structured data input, and use an automatic grammar-based test generator to produce a set of well-distributed test cases, each of which is equipped with a set of structural features. We show that structural features can be effectively used as test coverage criteria for test suite reduction. By learning structural features associated with failed test cases, we present an automatic fault localization approach to find out software defects which result in the testing failures. Preliminary experiments justify that our fault localization approach is able to accurately locate fault-inducing patterns.

Original languageEnglish (US)
Title of host publicationProceedings of the 29th Annual ACM Symposium on Applied Computing, SAC 2014
PublisherAssociation for Computing Machinery
Number of pages8
ISBN (Print)9781450324694
StatePublished - 2014
Event29th Annual ACM Symposium on Applied Computing, SAC 2014 - Gyeongju, Korea, Republic of
Duration: Mar 24 2014Mar 28 2014

Publication series

NameProceedings of the ACM Symposium on Applied Computing


Conference29th Annual ACM Symposium on Applied Computing, SAC 2014
Country/TerritoryKorea, Republic of

ASJC Scopus subject areas

  • Software


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