Unsupervised Machine Learning Approaches for Test Suite Reduction
Ensuring quality and reliability mandates thorough software testing at every stage of the development cycle. As software systems grow in size, complexity, and functionality, the parallel expansion of the test suite leads to an inefficient utilization of computational power and time, presenting chall...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | https://www.tandfonline.com/doi/10.1080/08839514.2024.2322336 |
| _version_ | 1826931011762520064 |
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| author | Anila Sebastian Hira Naseem Cagatay Catal |
| author_facet | Anila Sebastian Hira Naseem Cagatay Catal |
| author_sort | Anila Sebastian |
| collection | DOAJ |
| description | Ensuring quality and reliability mandates thorough software testing at every stage of the development cycle. As software systems grow in size, complexity, and functionality, the parallel expansion of the test suite leads to an inefficient utilization of computational power and time, presenting challenges to optimization. Therefore, the Test Suite Reduction (TSR) process is of great importance, contributing to the reduction of time and costs in executing test suites for complex software by minimizing the number of test cases to be executed. Over the past decade, machine learning-based solutions have emerged, demonstrating remarkable effectiveness and efficiency. Recent studies have delved into the application of Machine Learning (ML) in the software testing domain, where the high cost and time consumption associated with data annotation have prompted the use of unsupervised algorithms. In this research, we conducted a Systematic Mapping Study (SMS), examining the types of unsupervised algorithms implemented in developed models and thoroughly exploring the evaluation metrics employed. This study highlighted the prevalence of the K-Means clustering algorithm and the coverage metric for validation in various studies. Additionally, we identified a gap in the literature regarding scalability considerations. Our findings underscore the effective use of unsupervised learning approaches in test suite reduction. |
| first_indexed | 2024-03-07T16:17:39Z |
| format | Article |
| id | doaj.art-32f4c56c52b34f4eb0dfa8fe13f2fa90 |
| institution | Directory Open Access Journal |
| issn | 0883-9514 1087-6545 |
| language | English |
| last_indexed | 2025-02-17T16:39:03Z |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj.art-32f4c56c52b34f4eb0dfa8fe13f2fa902024-12-16T16:13:01ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452024-12-0138110.1080/08839514.2024.2322336Unsupervised Machine Learning Approaches for Test Suite ReductionAnila Sebastian0Hira Naseem1Cagatay Catal2Department of Computer Science and Engineering, College of Engineering, Qatar University, Doha, QatarDepartment of Computer Science and Engineering, College of Engineering, Qatar University, Doha, QatarDepartment of Computer Science and Engineering, College of Engineering, Qatar University, Doha, QatarEnsuring quality and reliability mandates thorough software testing at every stage of the development cycle. As software systems grow in size, complexity, and functionality, the parallel expansion of the test suite leads to an inefficient utilization of computational power and time, presenting challenges to optimization. Therefore, the Test Suite Reduction (TSR) process is of great importance, contributing to the reduction of time and costs in executing test suites for complex software by minimizing the number of test cases to be executed. Over the past decade, machine learning-based solutions have emerged, demonstrating remarkable effectiveness and efficiency. Recent studies have delved into the application of Machine Learning (ML) in the software testing domain, where the high cost and time consumption associated with data annotation have prompted the use of unsupervised algorithms. In this research, we conducted a Systematic Mapping Study (SMS), examining the types of unsupervised algorithms implemented in developed models and thoroughly exploring the evaluation metrics employed. This study highlighted the prevalence of the K-Means clustering algorithm and the coverage metric for validation in various studies. Additionally, we identified a gap in the literature regarding scalability considerations. Our findings underscore the effective use of unsupervised learning approaches in test suite reduction.https://www.tandfonline.com/doi/10.1080/08839514.2024.2322336 |
| spellingShingle | Anila Sebastian Hira Naseem Cagatay Catal Unsupervised Machine Learning Approaches for Test Suite Reduction Applied Artificial Intelligence |
| title | Unsupervised Machine Learning Approaches for Test Suite Reduction |
| title_full | Unsupervised Machine Learning Approaches for Test Suite Reduction |
| title_fullStr | Unsupervised Machine Learning Approaches for Test Suite Reduction |
| title_full_unstemmed | Unsupervised Machine Learning Approaches for Test Suite Reduction |
| title_short | Unsupervised Machine Learning Approaches for Test Suite Reduction |
| title_sort | unsupervised machine learning approaches for test suite reduction |
| url | https://www.tandfonline.com/doi/10.1080/08839514.2024.2322336 |
| work_keys_str_mv | AT anilasebastian unsupervisedmachinelearningapproachesfortestsuitereduction AT hiranaseem unsupervisedmachinelearningapproachesfortestsuitereduction AT cagataycatal unsupervisedmachinelearningapproachesfortestsuitereduction |