Hybrid Adaptive Bat and Particle Swarm Approach for Activity Diagram Based Test Case Generation.
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| Title: | Hybrid Adaptive Bat and Particle Swarm Approach for Activity Diagram Based Test Case Generation. |
|---|---|
| Authors: | Sahoo, Rajesh Kumar1, Nayak, Sanjib Kumar2, Upadhyay, Santosh Kumar2 upadhyaysantosh@akgec.ac.in, Ojha, Deeptimanta3, P., Pawan Kumar4 |
| Source: | International Journal of Performability Engineering. Apr2026, Vol. 22 Issue 4, p227-235. 9p. |
| Subjects: | Computer software testing, Metaheuristic algorithms, Flow charts, Withdrawal of funds, Mathematical optimization |
| Abstract: | Software testing has always been an essential pillar in ensuring software reliability and satisfaction of user requirements. Software systems are complex and require thorough testing to improve reliability and quality. However, manual test case design has a notorious history of being time-consuming and is subject to human error. Even most available automated methods are inflexible and require significant time, effort, and financial resources. Recently, search-based test data generation has become a significant, effective, and practical approach to overcoming these obstacles, and many meta-heuristic algorithms have been proposed to generate test cases to achieve branch coverage. Even though these strategies have shown good performance, researchers can further optimize these approaches. This paper proposes an automated test-case generation and optimization model that integrates activity diagram modelling with a Hybrid Adaptive Bat Particle Swarm Algorithm (ABPSA). Activity diagrams are used to represent the system's dynamic behavior, while the ABPSA is a synergistic combination of the exploratory nature of the Bat algorithm and the adaptive optimization of Particle Swarm Optimization. The algorithm is aimed at dynamically tracking the development of the activity-diagram model and thus effectively producing optimized test data. The effectiveness of the given framework is empirically demonstrated through a case study of the ATM withdrawal process. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Performability Engineering is the property of Totem Publisher, Inc. 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 |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 192800076 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Hybrid Adaptive Bat and Particle Swarm Approach for Activity Diagram Based Test Case Generation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sahoo%2C+Rajesh+Kumar%22">Sahoo, Rajesh Kumar</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Nayak%2C+Sanjib+Kumar%22">Nayak, Sanjib Kumar</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Upadhyay%2C+Santosh+Kumar%22">Upadhyay, Santosh Kumar</searchLink><relatesTo>2</relatesTo><i> upadhyaysantosh@akgec.ac.in</i><br /><searchLink fieldCode="AR" term="%22Ojha%2C+Deeptimanta%22">Ojha, Deeptimanta</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22P%2E%2C+Pawan+Kumar%22">P., Pawan Kumar</searchLink><relatesTo>4</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Performability+Engineering%22">International Journal of Performability Engineering</searchLink>. Apr2026, Vol. 22 Issue 4, p227-235. 9p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Computer+software+testing%22">Computer software testing</searchLink><br /><searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Flow+charts%22">Flow charts</searchLink><br /><searchLink fieldCode="DE" term="%22Withdrawal+of+funds%22">Withdrawal of funds</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Software testing has always been an essential pillar in ensuring software reliability and satisfaction of user requirements. Software systems are complex and require thorough testing to improve reliability and quality. However, manual test case design has a notorious history of being time-consuming and is subject to human error. Even most available automated methods are inflexible and require significant time, effort, and financial resources. Recently, search-based test data generation has become a significant, effective, and practical approach to overcoming these obstacles, and many meta-heuristic algorithms have been proposed to generate test cases to achieve branch coverage. Even though these strategies have shown good performance, researchers can further optimize these approaches. This paper proposes an automated test-case generation and optimization model that integrates activity diagram modelling with a Hybrid Adaptive Bat Particle Swarm Algorithm (ABPSA). Activity diagrams are used to represent the system's dynamic behavior, while the ABPSA is a synergistic combination of the exploratory nature of the Bat algorithm and the adaptive optimization of Particle Swarm Optimization. The algorithm is aimed at dynamically tracking the development of the activity-diagram model and thus effectively producing optimized test data. The effectiveness of the given framework is empirically demonstrated through a case study of the ATM withdrawal process. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Performability Engineering is the property of Totem Publisher, Inc. 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: 10.23940/ijpe.26.04.p6.227235 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 227 Subjects: – SubjectFull: Computer software testing Type: general – SubjectFull: Metaheuristic algorithms Type: general – SubjectFull: Flow charts Type: general – SubjectFull: Withdrawal of funds Type: general – SubjectFull: Mathematical optimization Type: general Titles: – TitleFull: Hybrid Adaptive Bat and Particle Swarm Approach for Activity Diagram Based Test Case Generation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sahoo, Rajesh Kumar – PersonEntity: Name: NameFull: Nayak, Sanjib Kumar – PersonEntity: Name: NameFull: Upadhyay, Santosh Kumar – PersonEntity: Name: NameFull: Ojha, Deeptimanta – PersonEntity: Name: NameFull: P., Pawan Kumar IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 09731318 Numbering: – Type: volume Value: 22 – Type: issue Value: 4 Titles: – TitleFull: International Journal of Performability Engineering Type: main |
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