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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2505.18216 |
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| _version_ | 1866912391897284608 |
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| author | Cellier, Peggy Ducassé, Mireille Ferré, Sébastien Ridoux, Olivier Wong, W. Eric |
| author_facet | Cellier, Peggy Ducassé, Mireille Ferré, Sébastien Ridoux, Olivier Wong, W. Eric |
| contents | This chapter illustrates the basic concepts of fault localization using a data mining technique. It utilizes the Trityp program to illustrate the general method. Formal concept analysis and association rule are two well-known methods for symbolic data mining. In their original inception, they both consider data in the form of an object-attribute table. In their original inception, they both consider data in the form of an object-attribute table. The chapter considers a debugging process in which a program is tested against different test cases. Two attributes, PASS and FAIL, represent the issue of the test case. The chapter extends the analysis of data mining for fault localization for the multiple fault situations. It addresses how data mining can be further applied to fault localization for GUI components. Unlike traditional software, GUI test cases are usually event sequences, and each individual event has a unique corresponding event handler. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_18216 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data Mining-Based Techniques for Software Fault Localization Cellier, Peggy Ducassé, Mireille Ferré, Sébastien Ridoux, Olivier Wong, W. Eric Software Engineering Artificial Intelligence This chapter illustrates the basic concepts of fault localization using a data mining technique. It utilizes the Trityp program to illustrate the general method. Formal concept analysis and association rule are two well-known methods for symbolic data mining. In their original inception, they both consider data in the form of an object-attribute table. In their original inception, they both consider data in the form of an object-attribute table. The chapter considers a debugging process in which a program is tested against different test cases. Two attributes, PASS and FAIL, represent the issue of the test case. The chapter extends the analysis of data mining for fault localization for the multiple fault situations. It addresses how data mining can be further applied to fault localization for GUI components. Unlike traditional software, GUI test cases are usually event sequences, and each individual event has a unique corresponding event handler. |
| title | Data Mining-Based Techniques for Software Fault Localization |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2505.18216 |