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• W4384159069 abstract "To speed up the bug-fixing process, it is essential to triage bugs into the right components as soon as possible. Given the large number of bugs filed everyday, a reliable and effective bug-component triaging tool is needed to assist this task. LR-BKG is the state-of-the-art toolkit for doing this. However, the suboptimal performance for recommending the right component at the first position (low Top-1 accuracy) limits its usage in practice. We thoroughly investigate the limitations of LR-BKG and find out the gap between the manual feature design of LR-BKG and the characteristics of bug reports causes such suboptimal performance. Therefore, we propose an approach, DEEPTRIAG, which uses the large scale pre-trained models to extract deep features automatically from bug reports (including bug summary and description), to fill this gap. DEEPTRIAG transforms bug-component triaging into a multi-classification task (CodeBERT-Classifier) and a generation task (CodeT5-Generator). Then, we ensemble the prediction results from them to improve the performance of bug-component triaging further. Extensive experimental results demonstrate the superior performance of DEEPTRIAG on bug-component triaging over LR-BKG. In particular, the overall Top-1 accuracy is improved from 56.2% to 68.3% on Mozilla dataset and from 51.3% to 64.1% on Eclipse dataset, which verifies the effectiveness and generalization of our approach on improving the practical usage for bug-component triaging." @default.
• W4384159069 created "2023-07-14" @default.
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• W4384159069 date "2023-05-01" @default.
• W4384159069 modified "2023-10-08" @default.
• W4384159069 title "Still Confusing for Bug-Component Triaging? Deep Feature Learning and Ensemble Setting to Rescue" @default.
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• W4384159069 doi "https://doi.org/10.1109/icpc58990.2023.00046" @default.
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