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- W4312556311 abstract "Classifying production and test bug reports can significantly improve not only the accuracy of performance evaluation but also the performance of information retrieval-based bug localization (IRBL). However, it is time-consuming for developers to classify these bug reports manually. This study proposes a production and test bug report classification method based on deep learning. Our method uses a set of source files and model tuning to solve the problem of insufficient and sparse bug reports when applying deep learning. Our experimental results reveal that the macro f1-score of our method is 0.84 and can improve the IRBL performance by 20%." @default.
- W4312556311 created "2023-01-05" @default.
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- W4312556311 date "2022-05-21" @default.
- W4312556311 modified "2023-09-30" @default.
- W4312556311 title "Deep learning-based production and test bug report classification using source files" @default.
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- W4312556311 doi "https://doi.org/10.1145/3510454.3528646" @default.
- W4312556311 hasPublicationYear "2022" @default.
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