Academic Journal
SQUMUTH squirrel search based algorithm for high order mutant generation in mutation testing.
| Τίτλος: | SQUMUTH squirrel search based algorithm for high order mutant generation in mutation testing. |
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| Συγγραφείς: | Mohanty, Subhasish1 (AUTHOR) subhasish2307@gmail.com, Mishra, Jyotirmaya1 (AUTHOR) jyotirmayamishra75@gmail.com, Mohapatra, Sudhir Kumar2 (AUTHOR) sudhir.mohapatra@srisriuniversity.edu.in, Bejo, Seifu Detso3 (AUTHOR) seifu.detso@wku.edu.et, Deferisha, Aliazar Deneke4 (AUTHOR) aliazar.deneke@amu.edu.et |
| Πηγή: | Discover Computing. Dec2025, Vol. 28 Issue 1, p1-17. 17p. |
| Θεματικοί όροι: | *Computer software testing, *Mutations (Algebra), *Mathematical optimization, *Foraging behavior, *Heuristic |
| Περίληψη: | In today's software testing community, quality assessment remains critical, with mutation testing standing as a cornerstone technique for evaluating the effectiveness of test cases. This method involves introducing faulty code, or mutants, into the program to assess the quality of the test suite and other testing methods. However, mutation testing faces challenges such as the generation of numerous mutants, the presence of equivalent mutants that are difficult to detect through testing, and the lack of realistic mutant creation. Literature reviews indicate significant efforts to address these issues through formal solutions and heuristic methods. Recent optimization-based methods are now recognized as cost-effective result in an optimized solution. Hence, to overcome these limitations, this study introduces SQUMUTH, a novel approach for high-order mutant generation based on the Squirrel Search Algorithm (SSA). Inspired by the foraging behavior of squirrels, SSA offers a promising solution for enhancing the efficiency and effectiveness of mutation testing. Experimental evaluations on eight well-known Java benchmark programs demonstrate that SQUMUTH outperforms existing methods. Comparative analyses of mutation scores and the rates of realistic mutants consistently show its better performance across all subject programs compared to other state-of-the-art methods such as Social Group Optimization, Binary Genetic Algorithm, and random testing. The experimental results underscore its effectiveness in generating more realistic mutants. The experimental results indicated that the proposed approach has the potential to advance software testing by improving the cost-effectiveness of mutation analysis and the quality of software systems. [ABSTRACT FROM AUTHOR] |
| Βάση Δεδομένων: | Academic Search Index |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://dx.doi.org/doi:10.1007/s10791-025-09525-1 Name: EDS - Springer Nature Journals (s7799221) Category: fullText Text: View record at Springer |
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| Header | DbId: asx DbLabel: Academic Search Index An: 191013341 RelevancyScore: 1393 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1393.09606933594 |
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| Items | – Name: Title Label: Title Group: Ti Data: SQUMUTH squirrel search based algorithm for high order mutant generation in mutation testing. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Mohanty%2C+Subhasish%22">Mohanty, Subhasish</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> subhasish2307@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Mishra%2C+Jyotirmaya%22">Mishra, Jyotirmaya</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> jyotirmayamishra75@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Mohapatra%2C+Sudhir+Kumar%22">Mohapatra, Sudhir Kumar</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> sudhir.mohapatra@srisriuniversity.edu.in</i><br /><searchLink fieldCode="AR" term="%22Bejo%2C+Seifu+Detso%22">Bejo, Seifu Detso</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> seifu.detso@wku.edu.et</i><br /><searchLink fieldCode="AR" term="%22Deferisha%2C+Aliazar+Deneke%22">Deferisha, Aliazar Deneke</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> aliazar.deneke@amu.edu.et</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Discover+Computing%22">Discover Computing</searchLink>. Dec2025, Vol. 28 Issue 1, p1-17. 17p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Computer+software+testing%22">Computer software testing</searchLink><br />*<searchLink fieldCode="DE" term="%22Mutations+%28Algebra%29%22">Mutations (Algebra)</searchLink><br />*<searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br />*<searchLink fieldCode="DE" term="%22Foraging+behavior%22">Foraging behavior</searchLink><br />*<searchLink fieldCode="DE" term="%22Heuristic%22">Heuristic</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In today's software testing community, quality assessment remains critical, with mutation testing standing as a cornerstone technique for evaluating the effectiveness of test cases. This method involves introducing faulty code, or mutants, into the program to assess the quality of the test suite and other testing methods. However, mutation testing faces challenges such as the generation of numerous mutants, the presence of equivalent mutants that are difficult to detect through testing, and the lack of realistic mutant creation. Literature reviews indicate significant efforts to address these issues through formal solutions and heuristic methods. Recent optimization-based methods are now recognized as cost-effective result in an optimized solution. Hence, to overcome these limitations, this study introduces SQUMUTH, a novel approach for high-order mutant generation based on the Squirrel Search Algorithm (SSA). Inspired by the foraging behavior of squirrels, SSA offers a promising solution for enhancing the efficiency and effectiveness of mutation testing. Experimental evaluations on eight well-known Java benchmark programs demonstrate that SQUMUTH outperforms existing methods. Comparative analyses of mutation scores and the rates of realistic mutants consistently show its better performance across all subject programs compared to other state-of-the-art methods such as Social Group Optimization, Binary Genetic Algorithm, and random testing. The experimental results underscore its effectiveness in generating more realistic mutants. The experimental results indicated that the proposed approach has the potential to advance software testing by improving the cost-effectiveness of mutation analysis and the quality of software systems. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asx&AN=191013341 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10791-025-09525-1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 1 Subjects: – SubjectFull: Computer software testing Type: general – SubjectFull: Mutations (Algebra) Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Foraging behavior Type: general – SubjectFull: Heuristic Type: general Titles: – TitleFull: SQUMUTH squirrel search based algorithm for high order mutant generation in mutation testing. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Mohanty, Subhasish – PersonEntity: Name: NameFull: Mishra, Jyotirmaya – PersonEntity: Name: NameFull: Mohapatra, Sudhir Kumar – PersonEntity: Name: NameFull: Bejo, Seifu Detso – PersonEntity: Name: NameFull: Deferisha, Aliazar Deneke IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 29482992 Numbering: – Type: volume Value: 28 – Type: issue Value: 1 Titles: – TitleFull: Discover Computing Type: main |
| ResultId | 1 |