Prompting Is All You Need: Automated Android Bug Replay with Large Language Models
Bug reports are vital for software maintenance that allow users to inform developers of the problems encountered while using the software. As such, researchers have committed considerable resources toward automating bug replay to expedite the process of software maintenance. Nonetheless, the success...
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| Vydáno v: | Proceedings / International Conference on Software Engineering s. 803 - 815 |
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ACM
14.04.2024
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| ISSN: | 1558-1225 |
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| Abstract | Bug reports are vital for software maintenance that allow users to inform developers of the problems encountered while using the software. As such, researchers have committed considerable resources toward automating bug replay to expedite the process of software maintenance. Nonetheless, the success of current au-tomated approaches is largely dictated by the characteristics and quality of bug reports, as they are constrained by the limitations of manually-crafted patterns and predefined vocabulary lists. In-spired by the success of Large Language Models (LLMs) in natural language understanding, we propose AdbGPT, a new lightweight approach to automatically reproduce the bugs from bug reports through prompt engineering, without any training and hard-coding effort. AdbGPT leverages few-shot learning and chain-of-thought reasoning to elicit human knowledge and logical reasoning from LLMs to accomplish the bug replay in a manner similar to a devel-oper. Our evaluations demonstrate the effectiveness and efficiency of our AdbGPT to reproduce 81.3% of bug reports in 253.6 seconds, outperforming the state-of-the-art baselines and ablation studies. We also conduct a small-scale user study to confirm the usefulness of AdbGPT in enhancing developers' bug replay capabilities. |
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| AbstractList | Bug reports are vital for software maintenance that allow users to inform developers of the problems encountered while using the software. As such, researchers have committed considerable resources toward automating bug replay to expedite the process of software maintenance. Nonetheless, the success of current au-tomated approaches is largely dictated by the characteristics and quality of bug reports, as they are constrained by the limitations of manually-crafted patterns and predefined vocabulary lists. In-spired by the success of Large Language Models (LLMs) in natural language understanding, we propose AdbGPT, a new lightweight approach to automatically reproduce the bugs from bug reports through prompt engineering, without any training and hard-coding effort. AdbGPT leverages few-shot learning and chain-of-thought reasoning to elicit human knowledge and logical reasoning from LLMs to accomplish the bug replay in a manner similar to a devel-oper. Our evaluations demonstrate the effectiveness and efficiency of our AdbGPT to reproduce 81.3% of bug reports in 253.6 seconds, outperforming the state-of-the-art baselines and ablation studies. We also conduct a small-scale user study to confirm the usefulness of AdbGPT in enhancing developers' bug replay capabilities. |
| Author | Chen, Chunyang Feng, Sidong |
| Author_xml | – sequence: 1 givenname: Sidong surname: Feng fullname: Feng, Sidong email: sidong.feng@monash.edu organization: Monash University,Melbourne,Australia – sequence: 2 givenname: Chunyang surname: Chen fullname: Chen, Chunyang email: chunyang.chen@monash.edu organization: Monash University,Melbourne,Australia |
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| Snippet | Bug reports are vital for software maintenance that allow users to inform developers of the problems encountered while using the software. As such, researchers... |
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| SubjectTerms | automated bug replay Cognition Computer bugs large language model Manuals Natural language processing prompt engineering Software maintenance Training Vocabulary |
| Title | Prompting Is All You Need: Automated Android Bug Replay with Large Language Models |
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