{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T01:51:08Z","timestamp":1784080268825,"version":"3.55.0"},"reference-count":29,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["WEB"],"published-print":{"date-parts":[[2024,4,22]]},"abstract":"<jats:p>Testing and debugging have been the most significant steps of software development since it is tricky for engineers to create error-free software. Software testing takes place after coding with the goal of finding flaws. If errors are found, debugging would be done to identify the source of the errors so that they may be fixed. Detecting as well as locating defects are thus two essential stages in the creation of software. We have created a unique approach with the following two working phases to generate a minimized test suite that is capable of both detecting and localizing faults. In the initial test suite minimization process, the cases were generated and minimized based on the objectives such as D-score and coverage by the utilization of the proposed Blue Monkey Customized Black Widow (BMCBW) algorithm. After this test suite minimization, the fault validation is done which includes the process of fault detection and localization. For this fault validation, we have utilized an improved Long Short-Term Memory (LSTM). At 90% of the learning rate the accuracy of the presented work is 0.97%, 2.20%, 2.52%, 0.97% and 2.81% is better than the other extant models like AOA, COOT, BES, BMO and BWO methods. The results obtained proved that our Blue Monkey Customized Black Widow Optimization-based fault detection and localization approach can provide superior outcomes.<\/jats:p>","DOI":"10.3233\/web-220131","type":"journal-article","created":{"date-parts":[[2023,8,29]],"date-time":"2023-08-29T11:42:26Z","timestamp":1693309346000},"page":"151-166","source":"Crossref","is-referenced-by-count":3,"title":["Test suite optimization under multi-objective constraints for software fault detection and localization: Hybrid optimization based model"],"prefix":"10.1177","volume":"22","author":[{"given":"Adline","family":"Freeda R","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Hindustan Institute of Technology & Science, Rajiv Gandhi Salai (OMR), Padur, Kelambakkam, Tamil Nadu 603103, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Selvi","family":"Rajendran P","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Hindustan Institute of Technology & Science, Rajiv Gandhi Salai (OMR), Padur, Kelambakkam, Tamil Nadu 603103, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"key":"10.3233\/WEB-220131_ref2","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-16-7610-9_7"},{"key":"10.3233\/WEB-220131_ref3","doi-asserted-by":"crossref","unstructured":"A.\u00a0Bajaj and O.\u00a0Prakash Sangwan, Discrete cuckoo search algorithms for test case prioritization, Applied Soft Computing 110 (2021).","DOI":"10.1016\/j.asoc.2021.107584"},{"key":"10.3233\/WEB-220131_ref4","doi-asserted-by":"crossref","unstructured":"J.\u00a0Cao et al., DeepFD: Automated fault diagnosis and localization for deep learning programs, in: Proceedings of the 44th International Conference on Software Engineering, 2022.","DOI":"10.1145\/3510003.3510099"},{"key":"10.3233\/WEB-220131_ref5","doi-asserted-by":"crossref","unstructured":"K.\u00a0Chakradhar Reddy, 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