Hybrid genetic algorithm for improving fault localization
Finding faults in a program correctly is crucial in software maintenance. In this light, many techniques have been proposed such as program slicing, code coverage, program state and mutation analysis. While all these techniques give us good insight on fault localization, but it appears that these te...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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American Scientific Publishers
2018
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| Online Access: | http://psasir.upm.edu.my/id/eprint/64692/ http://psasir.upm.edu.my/id/eprint/64692/1/Hybrid%20genetic%20algorithm%20for%20improving%20fault%20localization.pdf |
| _version_ | 1848855070718820352 |
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| author | Mahamad Zakaria, Muhammad Luqman Sharif, Khaironi Yatim Abd Ghani, Abdul Azim Koh, Tieng Wei Zulzalil, Hazura |
| author_facet | Mahamad Zakaria, Muhammad Luqman Sharif, Khaironi Yatim Abd Ghani, Abdul Azim Koh, Tieng Wei Zulzalil, Hazura |
| author_sort | Mahamad Zakaria, Muhammad Luqman |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Finding faults in a program correctly is crucial in software maintenance. In this light, many techniques have been proposed such as program slicing, code coverage, program state and mutation analysis. While all these techniques give us good insight on fault localization, but it appears that these techniques are made based on the assumption that the faults are caused by a single fault. However, in a reality, one fault could also possibly caused by multiple faults. This requires a technique which is specifically designed to handle multiple faults. With this regards, application of mutation analysis to localize faults might generate a vast number of mutants. As a result, these will lead to difficulty in choosing important mutants that are capable of localizing faults. Therefore, there is a need for a technique which able to localize a fault effectively with less number of mutants generated. Genetic algorithm (GA) is well known in finding an optimal solution to a problem while local search is capable of removing duplication. Since both had their own advantage, we have combined both techniques to enhance multiple localization of software fault. The result of the experiment shows that our technique able to detect multiple faults in various java programs but there is a need for improvement especially in prioritize fault that occurs simultaneously. |
| first_indexed | 2025-11-15T11:19:55Z |
| format | Article |
| id | upm-64692 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T11:19:55Z |
| publishDate | 2018 |
| publisher | American Scientific Publishers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-646922018-08-14T02:39:23Z http://psasir.upm.edu.my/id/eprint/64692/ Hybrid genetic algorithm for improving fault localization Mahamad Zakaria, Muhammad Luqman Sharif, Khaironi Yatim Abd Ghani, Abdul Azim Koh, Tieng Wei Zulzalil, Hazura Finding faults in a program correctly is crucial in software maintenance. In this light, many techniques have been proposed such as program slicing, code coverage, program state and mutation analysis. While all these techniques give us good insight on fault localization, but it appears that these techniques are made based on the assumption that the faults are caused by a single fault. However, in a reality, one fault could also possibly caused by multiple faults. This requires a technique which is specifically designed to handle multiple faults. With this regards, application of mutation analysis to localize faults might generate a vast number of mutants. As a result, these will lead to difficulty in choosing important mutants that are capable of localizing faults. Therefore, there is a need for a technique which able to localize a fault effectively with less number of mutants generated. Genetic algorithm (GA) is well known in finding an optimal solution to a problem while local search is capable of removing duplication. Since both had their own advantage, we have combined both techniques to enhance multiple localization of software fault. The result of the experiment shows that our technique able to detect multiple faults in various java programs but there is a need for improvement especially in prioritize fault that occurs simultaneously. American Scientific Publishers 2018 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/64692/1/Hybrid%20genetic%20algorithm%20for%20improving%20fault%20localization.pdf Mahamad Zakaria, Muhammad Luqman and Sharif, Khaironi Yatim and Abd Ghani, Abdul Azim and Koh, Tieng Wei and Zulzalil, Hazura (2018) Hybrid genetic algorithm for improving fault localization. Advanced Science Letters, 24 (3). pp. 1587-1590. ISSN 1936-6612; ESSN: 1936-7317 https://www.ingentaconnect.com/contentone/asp/asl/2018/00000024/00000003/art00012 10.1166/asl.2018.11115 |
| spellingShingle | Mahamad Zakaria, Muhammad Luqman Sharif, Khaironi Yatim Abd Ghani, Abdul Azim Koh, Tieng Wei Zulzalil, Hazura Hybrid genetic algorithm for improving fault localization |
| title | Hybrid genetic algorithm for improving fault localization |
| title_full | Hybrid genetic algorithm for improving fault localization |
| title_fullStr | Hybrid genetic algorithm for improving fault localization |
| title_full_unstemmed | Hybrid genetic algorithm for improving fault localization |
| title_short | Hybrid genetic algorithm for improving fault localization |
| title_sort | hybrid genetic algorithm for improving fault localization |
| url | http://psasir.upm.edu.my/id/eprint/64692/ http://psasir.upm.edu.my/id/eprint/64692/ http://psasir.upm.edu.my/id/eprint/64692/ http://psasir.upm.edu.my/id/eprint/64692/1/Hybrid%20genetic%20algorithm%20for%20improving%20fault%20localization.pdf |