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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.17977 |
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| _version_ | 1866918331589591040 |
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| author | Zhu, Hao-Nan Furth, Robert M. Pradel, Michael Rubio-González, Cindy |
| author_facet | Zhu, Hao-Nan Furth, Robert M. Pradel, Michael Rubio-González, Cindy |
| contents | Software defect datasets, which are collections of software bugs, are essential resources to facilitate empirical research and enable standardized benchmarking for a wide range of software engineering techniques, including emerging areas like agentic AI-based software development. Over the years, numerous software defect datasets have been developed, providing rich resources for the community, yet making it increasingly difficult to navigate the landscape. This article provides a comprehensive survey of 151 software defect datasets, covering their scope, construction, availability, usability, and practical uses. We also suggest potential opportunities for future research based on our findings, such as addressing underrepresented kinds of defects. A complete catalog of all surveyed software defect datasets is available at https://defect-datasets.github.io/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_17977 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Bugs to Benchmarks: A Comprehensive Survey of Software Defect Datasets Zhu, Hao-Nan Furth, Robert M. Pradel, Michael Rubio-González, Cindy Software Engineering Software defect datasets, which are collections of software bugs, are essential resources to facilitate empirical research and enable standardized benchmarking for a wide range of software engineering techniques, including emerging areas like agentic AI-based software development. Over the years, numerous software defect datasets have been developed, providing rich resources for the community, yet making it increasingly difficult to navigate the landscape. This article provides a comprehensive survey of 151 software defect datasets, covering their scope, construction, availability, usability, and practical uses. We also suggest potential opportunities for future research based on our findings, such as addressing underrepresented kinds of defects. A complete catalog of all surveyed software defect datasets is available at https://defect-datasets.github.io/. |
| title | From Bugs to Benchmarks: A Comprehensive Survey of Software Defect Datasets |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2504.17977 |