Deep learning-based production and test bug report classification using source files
Misoo Kim, Youngkyoung Kim, Eunseok Lee · 2022
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%.