Identifying error prone paths in software with genetic algorithms

James R. Birt, Renate Sitte

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Abstract

Although various methods have been applied for detecting and reducing errors in software, little research has been done into partitioning a system into smaller, error prone domains for Software Quality Assurance. In this paper we demonstrate that it is possible to identify error prone parts in programs. This is important because by identifying them, they can be given priority in code inspections or testing. We use a scheme for allocating a potential Source of Error to the code instructions. Then we use Genetic Algorithms to find the most error prone parts in that software. We do this by observing to what extent the Genetic Algorithms identifies the heaviest error seeded paths by applying clustered error seeding strategies. We have compared the performance of the Genetic Algorithms in identifying error prone paths with random path selection. Results from our experiments show that the Genetic Algorithms can identify the most potentially error prone paths that would contain at least 85% of the clustered seeded errors.
Original languageEnglish
Title of host publicationProceedings of the IASTED international conference on Software Engineering
EditorsPeter Kokol
Place of PublicationCalgary, Canada
PublisherACTA Press
Pages270-275
Number of pages6
ISBN (Electronic)0-88986-574-4
ISBN (Print)9780889865723
Publication statusPublished - 2006
EventIASTED International Conference on Software Engineering: Applied Informatics - Innsbruck, Austria
Duration: 15 Feb 200517 Feb 2005
Conference number: 23rd
http://www.iasted.org/conferences/pastinfo-518.html

Conference

ConferenceIASTED International Conference on Software Engineering
Abbreviated titleSE 2006
CountryAustria
CityInnsbruck
Period15/02/0517/02/05
Internet address

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  • Cite this

    Birt, J. R., & Sitte, R. (2006). Identifying error prone paths in software with genetic algorithms. In P. Kokol (Ed.), Proceedings of the IASTED international conference on Software Engineering (pp. 270-275). ACTA Press. http://www.actapress.com/Abstract.aspx?paperId=23357