Effective test-data generation using the modified black widow optimization algorithm

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dc.authorscopusidAli Ghaffari / 57197223215
dc.authorscopusidArasteh, Bahman / 39861139000
dc.authorwosidAli Ghaffari / AAV-3651-2020
dc.authorwosidArasteh, Bahman / AAN-9555-2021
dc.contributor.authorArasteh, Bahman
dc.contributor.authorGhaffari, Ali
dc.contributor.authorKhadir, Milad
dc.contributor.authorTorkamanian-Afshar, Mahsa
dc.contributor.authorPirahesh, Sajad
dc.date.accessioned2025-04-18T10:50:56Z
dc.date.available2025-04-18T10:50:56Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümü
dc.description.abstractSoftware testing is one of the software development activities and is used to identify and remove software bugs. Most small-sized projects may be manually tested to find and fix any bugs. In large and real-world software products, manual testing is thought to be a time and money-consuming process. Finding a minimal subset of input data in the shortest amount of time (as test data) to obtain the maximal branch coverage is an NP-complete problem in the field. Different heuristic-based methods have been used to generate test data. In this paper, for addressing and solving the test data generation problem, the black widow optimization algorithm has been used. The branch coverage criterion was used as the fitness function to optimize the generated data. The obtained experimental results on the standard benchmarks show that the proposed method generates more effective test data than the simulated annealing, genetic algorithm, ant colony optimization, particle swarm optimization, and artificial bee colony algorithms. According to the results, with 99.98% average coverage, 99.96% success rate, and 9.36 required iteration, the method was able to outperform the other methods.
dc.identifier.citationArasteh, B., Ghaffari, A., Khadir, M., Torkamanian-Afshar, M., & Pirahesh, S. (2024). Effective test-data generation using the modified black widow optimization algorithm. Signal, Image and Video Processing, 1-14.
dc.identifier.doi10.1007/s11760-024-03236-8
dc.identifier.endpage5346
dc.identifier.issn1863-1703
dc.identifier.issn1863-1711
dc.identifier.issue6-7
dc.identifier.scopus2-s2.0-85192789371
dc.identifier.scopusqualityQ2
dc.identifier.startpage5333
dc.identifier.urihttp://dx.doi.org/10.1007/s11760-024-03236-8
dc.identifier.urihttps://hdl.handle.net/20.500.12713/7215
dc.identifier.volume18
dc.identifier.wosWOS:001220395200003
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorArasteh, Bahman
dc.institutionauthorGhaffari, Ali
dc.institutionauthoridAli Ghaffari / 0000-0001-5407-8629
dc.institutionauthoridArasteh, Bahman / 0000-0001-5202-6315
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofSignal Image and Video Processing
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectSoftware-Test Generation
dc.subjectBlack Widow Optimization Algorithm
dc.subjectBranch Coverage
dc.subjectSuccess Rate
dc.subjectStability
dc.titleEffective test-data generation using the modified black widow optimization algorithm
dc.typeArticle

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