AutoEmpirical: LLM-Based Automated Research for Empirical Software Fault Analysis
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909827222994944 |
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| author | Yu, Jiongchi Jiang, Weipeng Zhang, Xiaoyu Hu, Qiang Xie, Xiaofei Shen, Chao |
| author_facet | Yu, Jiongchi Jiang, Weipeng Zhang, Xiaoyu Hu, Qiang Xie, Xiaofei Shen, Chao |
| contents | Understanding software faults is essential for empirical research in software development and maintenance. However, traditional fault analysis, while valuable, typically involves multiple expert-driven steps such as collecting potential faults, filtering, and manual investigation. These processes are both labor-intensive and time-consuming, creating bottlenecks that hinder large-scale fault studies in complex yet critical software systems and slow the pace of iterative empirical research.
In this paper, we decompose the process of empirical software fault study into three key phases: (1) research objective definition, (2) data preparation, and (3) fault analysis, and we conduct an initial exploration study of applying Large Language Models (LLMs) for fault analysis of open-source software. Specifically, we perform the evaluation on 3,829 software faults drawn from a high-quality empirical study. Our results show that LLMs can substantially improve efficiency in fault analysis, with an average processing time of about two hours, compared to the weeks of manual effort typically required. We conclude by outlining a detailed research plan that highlights both the potential of LLMs for advancing empirical fault studies and the open challenges that required be addressed to achieve fully automated, end-to-end software fault analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_04997 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AutoEmpirical: LLM-Based Automated Research for Empirical Software Fault Analysis Yu, Jiongchi Jiang, Weipeng Zhang, Xiaoyu Hu, Qiang Xie, Xiaofei Shen, Chao Software Engineering Artificial Intelligence Understanding software faults is essential for empirical research in software development and maintenance. However, traditional fault analysis, while valuable, typically involves multiple expert-driven steps such as collecting potential faults, filtering, and manual investigation. These processes are both labor-intensive and time-consuming, creating bottlenecks that hinder large-scale fault studies in complex yet critical software systems and slow the pace of iterative empirical research. In this paper, we decompose the process of empirical software fault study into three key phases: (1) research objective definition, (2) data preparation, and (3) fault analysis, and we conduct an initial exploration study of applying Large Language Models (LLMs) for fault analysis of open-source software. Specifically, we perform the evaluation on 3,829 software faults drawn from a high-quality empirical study. Our results show that LLMs can substantially improve efficiency in fault analysis, with an average processing time of about two hours, compared to the weeks of manual effort typically required. We conclude by outlining a detailed research plan that highlights both the potential of LLMs for advancing empirical fault studies and the open challenges that required be addressed to achieve fully automated, end-to-end software fault analysis. |
| title | AutoEmpirical: LLM-Based Automated Research for Empirical Software Fault Analysis |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2510.04997 |