Enhancing Genetic Improvement of Software with Regression Test Selection.
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| Title: | Enhancing Genetic Improvement of Software with Regression Test Selection. |
|---|---|
| Authors: | Guizzo, Giovani1 fg.guizzo@ucl.ac.uk, Petke, Justyna1 j.petke@ucl.ac.uk, Sarro, Federica1 f.sarro@ucl.ac.uk, Harman, Mark1,2 mark.harman@ucl.ac.uk |
| Source: | ICSE: International Conference on Software Engineering. 5/22/2021, p1323-1333. 11p. |
| Subjects: | Artificial intelligence, Regression testing (Computer science), Computer software development, Software engineering, Genetic programming |
| Abstract: | Genetic improvement uses artificial intelligence to automatically improve software with respect to non-functional properties (AI for SE). In this paper, we propose the use of existing software engineering best practice to enhance Genetic Improvement (SE for AI). We conjecture that existing Regression Test Selection (RTS) techniques (which have been proven to be efficient and effective) can and should be used as a core component of the GI search process for maximising its effectiveness. To assess our idea, we have carried out a thorough empirical study assessing the use of both dynamic and static RTS techniques with GI to improve seven real-world software programs. The results of our empirical evaluation show that incorporation of RTS within GI significantly speeds up the whole GI process, making it up to 68% faster on our benchmark set, being still able to produce valid software improvements. Our findings are significant in that they can save hours to days of computational time, and can facilitate the uptake of GI in an industrial setting, by significantly reducing the time for the developer to receive feedback from such an automated technique. Therefore, we recommend the use of RTS in future test-based automated software improvement work. Finally, we hope this successful application of SE for AI will encourage other researchers to investigate further applications in this area. [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: Enhancing Genetic Improvement of Software with Regression Test Selection. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Guizzo%2C+Giovani%22">Guizzo, Giovani</searchLink><relatesTo>1</relatesTo><i> fg.guizzo@ucl.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Petke%2C+Justyna%22">Petke, Justyna</searchLink><relatesTo>1</relatesTo><i> j.petke@ucl.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Sarro%2C+Federica%22">Sarro, Federica</searchLink><relatesTo>1</relatesTo><i> f.sarro@ucl.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Harman%2C+Mark%22">Harman, Mark</searchLink><relatesTo>1,2</relatesTo><i> mark.harman@ucl.ac.uk</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>. 5/22/2021, p1323-1333. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+testing+%28Computer+science%29%22">Regression testing (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software+development%22">Computer software development</searchLink><br /><searchLink fieldCode="DE" term="%22Software+engineering%22">Software engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+programming%22">Genetic programming</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Genetic improvement uses artificial intelligence to automatically improve software with respect to non-functional properties (AI for SE). In this paper, we propose the use of existing software engineering best practice to enhance Genetic Improvement (SE for AI). We conjecture that existing Regression Test Selection (RTS) techniques (which have been proven to be efficient and effective) can and should be used as a core component of the GI search process for maximising its effectiveness. To assess our idea, we have carried out a thorough empirical study assessing the use of both dynamic and static RTS techniques with GI to improve seven real-world software programs. The results of our empirical evaluation show that incorporation of RTS within GI significantly speeds up the whole GI process, making it up to 68% faster on our benchmark set, being still able to produce valid software improvements. Our findings are significant in that they can save hours to days of computational time, and can facilitate the uptake of GI in an industrial setting, by significantly reducing the time for the developer to receive feedback from such an automated technique. Therefore, we recommend the use of RTS in future test-based automated software improvement work. Finally, we hope this successful application of SE for AI will encourage other researchers to investigate further applications in this area. [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: 10.1109/ICSE43902.2021.00120 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 1323 Subjects: – SubjectFull: Artificial intelligence Type: general – SubjectFull: Regression testing (Computer science) Type: general – SubjectFull: Computer software development Type: general – SubjectFull: Software engineering Type: general – SubjectFull: Genetic programming Type: general Titles: – TitleFull: Enhancing Genetic Improvement of Software with Regression Test Selection. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Guizzo, Giovani – PersonEntity: Name: NameFull: Petke, Justyna – PersonEntity: Name: NameFull: Sarro, Federica – PersonEntity: Name: NameFull: Harman, Mark IsPartOfRelationships: – BibEntity: Dates: – D: 22 M: 05 Text: 5/22/2021 Type: published Y: 2021 Titles: – TitleFull: ICSE: International Conference on Software Engineering Type: main |
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