How Does Pre-trained Language Model Perform on Deep Learning Framework Bug Prediction?
Pages 346 - 347
Abstract
Understanding and predicting bugs is crucial for developers seeking to enhance testing efficiency and mitigate issues in software releases. Bug reports, though semi-structured texts, contain a wealth of semantic information, rendering their comprehension a critical aspect of bug prediction. In light of the recent success of pre-trained language models (PLMs) in the domain of natural language processing, numerous studies have leveraged these models to grasp various forms of textual information. However, the capability of PLMs to understand bug reports remains uncertain. To tackle this challenge, we introduce KnowBug, a framework with a bug report knowledge-enhanced PLM. In this framework, utilizing bug reports obtained from open-source deep learning frameworks as input, prompts are designed and the PLM is fine-tuned for evaluating KnowBug's ability to comprehend bug reports and predict bug types.
References
[1]
Xiaoting Du, Yulei Sui, Zhihao Liu, and Jun Ai. 2023. An Empirical Study of Fault Triggers in Deep Learning Frameworks. IEEE Transactions on Dependable and Secure Computing 20, 4 (2023), 2696--2712.
[2]
Luiz Gomes, Ricardo da Silva Torres, and Mario Lúcio Côrtes. 2023. BERT-and TF-IDF-based feature extraction for long-lived bug prediction in FLOSS: a comparative study. Information and Software Technology 160 (2023), 107217.
[3]
Li Jia, Hao Zhong, Xiaoyin Wang, Linpeng Huang, and Xuansheng Lu. 2021. The symptoms, causes, and repairs of bugs inside a deep learning library. Journal of Systems and Software 177 (2021), 110935.
[4]
Ahmed Fawzi Otoom, Sara Al-jdaeh, and Maen Hammad. 2019. Automated Classification of Software Bug Reports. In Proceedings of the 9th International Conference on Information Communication and Management (Prague, Czech Republic) (ICICM 2019). Association for Computing Machinery, New York, NY, USA, 17--21.
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Published In
April 2024
531 pages
ISBN:9798400705021
DOI:10.1145/3639478
- Co-chairs:
- Ana Paiva,
- Rui Abreu,
- Program Co-chairs:
- Abhik Roychoudhury,
- Margaret Storey
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- Faculty of Engineering of University of Porto
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 23 May 2024
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- Short-paper
Funding Sources
- Fundamental Research Funds for the Central Universities
- Open Research Fund of Shanghai Key Laboratory of Trustworthy Computing (East China Normal University)
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ICSE-Companion '24
Sponsor:
ICSE-Companion '24: 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings
April 14 - 20, 2024
Lisbon, Portugal
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