iTree: Efficiently Discovering High-Coverage Configurations Using Interaction Trees.
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| Title: | iTree: Efficiently Discovering High-Coverage Configurations Using Interaction Trees. |
|---|---|
| Authors: | Song, Charles1 csfalcon@cs.umd.edu, Porter, Adam1 aporter@cs.umd.edu, Foster, Jeffrey S.1 jfoster@cs.umd.edu |
| Source: | ICSE: International Conference on Software Engineering. Feb2012, p903-913. 11p. |
| Subjects: | Software configuration management, Configuration management, Computer programming, Electronic data processing, Combinatorics, Computer software testing |
| Abstract: | Software configurability has many benefits, but it also makes programs much harder to test, as in the worst case the program must be tested under every possible configuration. One potential remedy to this problem is combinatorial interaction testing (CIT), in which typically the developer selects a strength t and then computes a covering array containing all t-way configuration option combinations. However, in a prior study we showed that several programs have important highstrength interactions (combinations of a subset of configuration options) that CIT is highly unlikely to generate in practice. In this paper, we propose a new algorithm called interaction tree discovery (iTree) that aims to identify sets of configurations to test that are smaller than those generated by CIT, while also including important high-strength interactions missed by practical applications of CIT. On each iteration of iTree, we first use low-strength CIT to test the program under a set of configurations, and then apply machine learning techniques to discover new interactions that are potentially responsible for any new coverage seen. By repeating this process, iTree builds up a set of configurations likely to contain key high-strength interactions. We evaluated iTree by comparing the coverage it achieves versus covering arrays and randomly generated configuration sets. Our results strongly suggest that iTree can identify high-coverage sets of configurations more effectively than traditional CIT or random sampling. [ABSTRACT FROM AUTHOR] |
| Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Items | – Name: Title Label: Title Group: Ti Data: iTree: Efficiently Discovering High-Coverage Configurations Using Interaction Trees. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Song%2C+Charles%22">Song, Charles</searchLink><relatesTo>1</relatesTo><i> csfalcon@cs.umd.edu</i><br /><searchLink fieldCode="AR" term="%22Porter%2C+Adam%22">Porter, Adam</searchLink><relatesTo>1</relatesTo><i> aporter@cs.umd.edu</i><br /><searchLink fieldCode="AR" term="%22Foster%2C+Jeffrey+S%2E%22">Foster, Jeffrey S.</searchLink><relatesTo>1</relatesTo><i> jfoster@cs.umd.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22ICSE%3A+International+Conference+on+Software+Engineering%22">ICSE: International Conference on Software Engineering</searchLink>. Feb2012, p903-913. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Software+configuration+management%22">Software configuration management</searchLink><br /><searchLink fieldCode="DE" term="%22Configuration+management%22">Configuration management</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+programming%22">Computer programming</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+data+processing%22">Electronic data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Combinatorics%22">Combinatorics</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software+testing%22">Computer software testing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Software configurability has many benefits, but it also makes programs much harder to test, as in the worst case the program must be tested under every possible configuration. One potential remedy to this problem is combinatorial interaction testing (CIT), in which typically the developer selects a strength t and then computes a covering array containing all t-way configuration option combinations. However, in a prior study we showed that several programs have important highstrength interactions (combinations of a subset of configuration options) that CIT is highly unlikely to generate in practice. In this paper, we propose a new algorithm called interaction tree discovery (iTree) that aims to identify sets of configurations to test that are smaller than those generated by CIT, while also including important high-strength interactions missed by practical applications of CIT. On each iteration of iTree, we first use low-strength CIT to test the program under a set of configurations, and then apply machine learning techniques to discover new interactions that are potentially responsible for any new coverage seen. By repeating this process, iTree builds up a set of configurations likely to contain key high-strength interactions. We evaluated iTree by comparing the coverage it achieves versus covering arrays and randomly generated configuration sets. Our results strongly suggest that iTree can identify high-coverage sets of configurations more effectively than traditional CIT or random sampling. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 903 Subjects: – SubjectFull: Software configuration management Type: general – SubjectFull: Configuration management Type: general – SubjectFull: Computer programming Type: general – SubjectFull: Electronic data processing Type: general – SubjectFull: Combinatorics Type: general – SubjectFull: Computer software testing Type: general Titles: – TitleFull: iTree: Efficiently Discovering High-Coverage Configurations Using Interaction Trees. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Song, Charles – PersonEntity: Name: NameFull: Porter, Adam – PersonEntity: Name: NameFull: Foster, Jeffrey S. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2012 Type: published Y: 2012 Titles: – TitleFull: ICSE: International Conference on Software Engineering Type: main |
| ResultId | 1 |
