Combinatorial test case prioritization using hybrid Energy Valley Dwarf Mongoose Optimization approach
Combinatorial Test Case Prioritization is a technique used in software testing to improve the efficiency and effectiveness of test suites. It involves selecting and ordering test cases based on their ability to detect faults, especially those caused by interactions between multiple parameters. Artif...
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| Vydané v: | Expert systems with applications Ročník 271; s. 126634 |
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| Jazyk: | English |
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Elsevier Ltd
01.05.2025
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| ISSN: | 0957-4174 |
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| Abstract | Combinatorial Test Case Prioritization is a technique used in software testing to improve the efficiency and effectiveness of test suites. It involves selecting and ordering test cases based on their ability to detect faults, especially those caused by interactions between multiple parameters. Artificial Intelligence (AI) has made significant contributions to Combinatorial Test Case Prioritization (TCP) by introducing advanced techniques to enhance the efficiency of the testing process. However, managing dependencies between test cases and adjusting the prioritization accordingly can be complex and time-consuming in most of the previous techniques. Therefore, an Energy Valley Dwarf Mongoose Optimization Algorithm (EVDMOA) is devised for Combinatorial TCP. Initially, the software programs are collected from the dataset. Then, test case generation is performed to create the test suites. Next, the combinatorial TCP is performed. Here, the fitness parameters such as Average Percentage of Fault Detected (APFD), Average Percentage of Branch Coverage (APBC), and weight are considered for fitness evaluation. Moreover, the weights in the fitness function are computed by the Deep Q Net (DQN), which is trained by the proposed EVDMOA. At last, the prioritized test cases are obtained. The EVDMOA achieves the AFPD, APBC, and fitness values of 0.907, 0.914, and 0.926. Moreover, the EVDMOA helps in maintaining the overall quality and reliability of the software. |
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| AbstractList | Combinatorial Test Case Prioritization is a technique used in software testing to improve the efficiency and effectiveness of test suites. It involves selecting and ordering test cases based on their ability to detect faults, especially those caused by interactions between multiple parameters. Artificial Intelligence (AI) has made significant contributions to Combinatorial Test Case Prioritization (TCP) by introducing advanced techniques to enhance the efficiency of the testing process. However, managing dependencies between test cases and adjusting the prioritization accordingly can be complex and time-consuming in most of the previous techniques. Therefore, an Energy Valley Dwarf Mongoose Optimization Algorithm (EVDMOA) is devised for Combinatorial TCP. Initially, the software programs are collected from the dataset. Then, test case generation is performed to create the test suites. Next, the combinatorial TCP is performed. Here, the fitness parameters such as Average Percentage of Fault Detected (APFD), Average Percentage of Branch Coverage (APBC), and weight are considered for fitness evaluation. Moreover, the weights in the fitness function are computed by the Deep Q Net (DQN), which is trained by the proposed EVDMOA. At last, the prioritized test cases are obtained. The EVDMOA achieves the AFPD, APBC, and fitness values of 0.907, 0.914, and 0.926. Moreover, the EVDMOA helps in maintaining the overall quality and reliability of the software. |
| ArticleNumber | 126634 |
| Author | Nithiyanandam, Prasath Shanmugam, Sangeetha Sekar, Saradha Kanagaraj, Kamaraj |
| Author_xml | – sequence: 1 givenname: Kamaraj orcidid: 0009-0005-0990-9709 surname: Kanagaraj fullname: Kanagaraj, Kamaraj email: kamaraj.rj@gmail.com organization: Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Arasur, Coimbatore 641010 Tamil Nadu, India – sequence: 2 givenname: Prasath surname: Nithiyanandam fullname: Nithiyanandam, Prasath email: prasath283@gmail.com organization: Department of Networking and Communications, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203 Tamil Nadu, India – sequence: 3 givenname: Saradha surname: Sekar fullname: Sekar, Saradha email: saradha.s@sece.ac.in organization: Department of Computer Science and Engineering, Sri Eshwar College of Engineering, Kinathukadavu, Coimbatore 642102 Tamil Nadu, India – sequence: 4 givenname: Sangeetha surname: Shanmugam fullname: Shanmugam, Sangeetha email: sangeetha@kce.ac.in organization: Department of Computer Science and Engineering, Karpagam College of Engineering, Coimbatore 641032 Tamil Nadu, India |
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| Cites_doi | 10.1016/j.asoc.2023.110185 10.1007/978-981-10-5687-1_58 10.32622/ijrat.752019218 10.1109/TSE.2020.2992428 10.1109/TSE.2009.52 10.1109/ACCESS.2019.2940620 10.3390/math8111857 10.1109/ICECET61485.2024.10698331 10.1371/journal.pone.0275346 10.1109/ACCESS.2019.2938260 10.1007/s00500-020-05517-z 10.1016/j.eswa.2015.04.055 10.1142/S0219649224500126 10.1016/j.ins.2017.03.007 10.1007/s10515-024-00447-8 10.1038/s41598-022-27344-y 10.1007/s00500-022-07121-9 10.1016/j.jss.2022.111419 10.1145/3539223 10.1109/TSE.2021.3070549 10.1016/j.asoc.2021.107584 10.3390/s22124374 10.1016/j.infsof.2017.08.014 10.1109/ICC45041.2023.10279598 10.1007/s00500-019-03795-w 10.1002/stv.430 |
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| Keywords | Dwarf Mongoose Optimization Algorithm (DMOA) Regression Testing Energy Valley Optimization (EVO) Test Case Prioritization (TCP) Deep Q Net (DQN) |
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