Prioritizing mutants to guide mutation testing.
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| Title: | Prioritizing mutants to guide mutation testing. |
|---|---|
| Authors: | Kaufman, Samuel J.1 kaufmans@cs.washington.edu, Featherman, Ryan1 feathr@cs.washington.edu, Alvin, Justin2 jalvin@umass.edu, Kurtz, Bob3 rkurtz22@gmu.edu, Ammann, Paul3 pammann@gmu.edu, Just, René1 rjust@cs.washington.edu |
| Source: | ICSE: International Conference on Software Engineering. 2022, p1743-1754. 12p. |
| Subjects: | Mutation testing of computer software, Concrete, Subset selection, Machine learning, Probability theory |
| Abstract: | Mutation testing offers concrete test goals (mutants) and a rigorous test efficacy criterion, but it is expensive due to vast numbers of mutants, many of which are neither useful nor actionable. Prior work has focused on selecting representative and sufficient mutant subsets, measuring whether a test set that is mutation-adequate for the subset is equally adequate for the entire set. However, no known industrial application of mutation testing uses or even computes mutation adequacy, instead focusing on iteratively presenting very few mutants as concrete test goals for developers to write tests. This paper (1) articulates important differences between mutation analysis, where measuring mutation adequacy is of interest, and mutation testing, where mutants are of interest insofar as they serve as concrete test goals to elict effective tests; (2) introduces a new measure of mutant usefulness, called test completeness advancement probability (TCAP); (3) introduces an approach to prioritizing mutants by incrementally selecting mutants based on their predicted TCAP; and (4) presents simulations showing that TCAP-based prioritization of mutants advances test completeness more rapidly than prioritization with the previous state-of-the-art. [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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| Header | DbId: egs DbLabel: Engineering Source An: 185195835 AccessLevel: 6 PubType: Conference PubTypeId: conference PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Prioritizing mutants to guide mutation testing. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kaufman%2C+Samuel+J%2E%22">Kaufman, Samuel J.</searchLink><relatesTo>1</relatesTo><i> kaufmans@cs.washington.edu</i><br /><searchLink fieldCode="AR" term="%22Featherman%2C+Ryan%22">Featherman, Ryan</searchLink><relatesTo>1</relatesTo><i> feathr@cs.washington.edu</i><br /><searchLink fieldCode="AR" term="%22Alvin%2C+Justin%22">Alvin, Justin</searchLink><relatesTo>2</relatesTo><i> jalvin@umass.edu</i><br /><searchLink fieldCode="AR" term="%22Kurtz%2C+Bob%22">Kurtz, Bob</searchLink><relatesTo>3</relatesTo><i> rkurtz22@gmu.edu</i><br /><searchLink fieldCode="AR" term="%22Ammann%2C+Paul%22">Ammann, Paul</searchLink><relatesTo>3</relatesTo><i> pammann@gmu.edu</i><br /><searchLink fieldCode="AR" term="%22Just%2C+René%22">Just, René</searchLink><relatesTo>1</relatesTo><i> rjust@cs.washington.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>. 2022, p1743-1754. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Mutation+testing+of+computer+software%22">Mutation testing of computer software</searchLink><br /><searchLink fieldCode="DE" term="%22Concrete%22">Concrete</searchLink><br /><searchLink fieldCode="DE" term="%22Subset+selection%22">Subset selection</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Probability+theory%22">Probability theory</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Mutation testing offers concrete test goals (mutants) and a rigorous test efficacy criterion, but it is expensive due to vast numbers of mutants, many of which are neither useful nor actionable. Prior work has focused on selecting representative and sufficient mutant subsets, measuring whether a test set that is mutation-adequate for the subset is equally adequate for the entire set. However, no known industrial application of mutation testing uses or even computes mutation adequacy, instead focusing on iteratively presenting very few mutants as concrete test goals for developers to write tests. This paper (1) articulates important differences between mutation analysis, where measuring mutation adequacy is of interest, and mutation testing, where mutants are of interest insofar as they serve as concrete test goals to elict effective tests; (2) introduces a new measure of mutant usefulness, called test completeness advancement probability (TCAP); (3) introduces an approach to prioritizing mutants by incrementally selecting mutants based on their predicted TCAP; and (4) presents simulations showing that TCAP-based prioritization of mutants advances test completeness more rapidly than prioritization with the previous state-of-the-art. [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: Identifiers: – Type: doi Value: 10.1145/3510003.3510187 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 1743 Subjects: – SubjectFull: Mutation testing of computer software Type: general – SubjectFull: Concrete Type: general – SubjectFull: Subset selection Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Probability theory Type: general Titles: – TitleFull: Prioritizing mutants to guide mutation testing. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kaufman, Samuel J. – PersonEntity: Name: NameFull: Featherman, Ryan – PersonEntity: Name: NameFull: Alvin, Justin – PersonEntity: Name: NameFull: Kurtz, Bob – PersonEntity: Name: NameFull: Ammann, Paul – PersonEntity: Name: NameFull: Just, René IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: 2022 Type: published Y: 2022 Titles: – TitleFull: ICSE: International Conference on Software Engineering Type: main |
| ResultId | 1 |
