Enhanced non-dominated sorting genetic algorithm for test case optimization
Also available in printed version
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | |
| التنسيق: | Master's thesis |
| اللغة: | الإنجليزية |
| منشور في: |
Universiti Teknologi Malaysia
2025
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://utmik.utm.my/handle/123456789/45461 |
| Abstract | Abstract here |
| _version_ | 1855605610863656960 |
|---|---|
| author | Izwan Mohd. Ismail |
| author2 | Wan Mohd. Nasir Wan Kadir, supervisor |
| author_facet | Wan Mohd. Nasir Wan Kadir, supervisor Izwan Mohd. Ismail |
| author_sort | Izwan Mohd. Ismail |
| description | Also available in printed version |
| format | Master's thesis |
| id | utm-123456789-45461 |
| institution | Universiti Teknologi Malaysia |
| language | English |
| publishDate | 2025 |
| publisher | Universiti Teknologi Malaysia |
| record_format | DSpace |
| record_pdf | Restricted |
| spelling | utm-123456789-454612025-08-21T14:08:02Z Enhanced non-dominated sorting genetic algorithm for test case optimization Izwan Mohd. Ismail Wan Mohd. Nasir Wan Kadir, supervisor Computing Also available in printed version Due to inevitable software changes, regression testing has become a crucial phase in software development process. Many software testers and researchers agreed that regression testing process consumes more time and cost during software development. Test case optimization has become one of the best solutions to overcome problems in regression testing. Test case optimization is focusing on reducing number of test cases in the test suite that may reduce the overall testing time, cost and effort of software testers. It considers multiple objectives and provides several numbers of optimal solution based on objectives of the testing. Therefore, this research aims at developing an alternative solution of test case optimization technique using NSGA II with fitness scaling as an additional function. Fitness scaling function is applied in NSGA II to eliminate pre-mature convergence among set of solution in the evolution of offspring in NSGA II which may produce more efficient fitness value. This research focuses on regression testing optimization by implementing weight of test cases and fault detection rate per test case as its objective function for optimization purposes. The proposed technique is applied to the GUI-based testing case study. The result shows that Pareto front produced by enhanced NSGA II give more wider set of solution that contains more alternatives and provide better trade-off among solutions. The evaluation shows that enhanced NSGA II perform better compared to conventional NSGA II by increasing the percentage of the reduced test cases with 25% and yield lower fault detection loss with 1.64% which indicating that set of reduced test cases using enhanced NSGA II is able to maintain the fault detection capability in the system under test fahmimoksen UTM 98 p. Thesis (Sarjana Sains (Sains Komputer)) - Universiti Teknologi Malaysia, 2018 2025-03-12T04:33:16Z 2025-03-12T04:33:16Z 2018 Master's thesis https://utmik.utm.my/handle/123456789/45461 vital:119433 valet-20190123-150138 ENG Closed Access UTM Complete Unpublished Completion application/pdf Universiti Teknologi Malaysia |
| spellingShingle | Computing Izwan Mohd. Ismail Enhanced non-dominated sorting genetic algorithm for test case optimization |
| thesis_level | Master |
| title | Enhanced non-dominated sorting genetic algorithm for test case optimization |
| title_full | Enhanced non-dominated sorting genetic algorithm for test case optimization |
| title_fullStr | Enhanced non-dominated sorting genetic algorithm for test case optimization |
| title_full_unstemmed | Enhanced non-dominated sorting genetic algorithm for test case optimization |
| title_short | Enhanced non-dominated sorting genetic algorithm for test case optimization |
| title_sort | enhanced non dominated sorting genetic algorithm for test case optimization |
| topic | Computing |
| url | https://utmik.utm.my/handle/123456789/45461 |
| work_keys_str_mv | AT izwanmohdismail enhancednondominatedsortinggeneticalgorithmfortestcaseoptimization |