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Empirically revisiting and enhancing automatic classification of bug and non-bug issues

Frontiers of Computer Science 2024年第5期18卷 25-44页

作者： Zhong LI Minxue PAN Yu PEI Tian ZHANG Linzhang WANG Xuandong LI State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China Department of Computer Science and Technology Nanjing UniversityNanjing 210023China Software Institute Nanjing UniversityNanjing 210093China Department of Computing The Hong Kong Polytechnic UniversityHong KongChina

A large body of research effort has been dedicated to automated issue classification for issue Tracking Systems(ITSs).Although the existing approaches have shown promising performance,the different design choices,including the different textual fields,feature representation methods and machine learning algorithms adopted by existing approaches,have not been comprehensively compared and *** fill this gap,we perform the first extensive study of automated issue classification on 9 state-of-the-art issue classification *** experimental results on the widely studied dataset reveal multiple practical guidelines for automated issue classification,including:(1)Training separate models for the issue titles and descriptions and then combining these two models tend to achieve better performance for issue classification;(2)Word embedding with Long Short-Term Memory(LSTM)can better extract features from the textual fields in the issues,and hence,lead to better issue classification models;(3)There exist certain terms in the textual fields that are helpful for building more discriminating classifiers between bug and non-bug issues;(4)The performance of the issue classification model is not sensitive to the choices of ML *** on our study outcomes,we further propose an advanced issue classification approach,DEEPLABEL,which can achieve better performance compared with the existing issue classification approaches.

关键词： issue tracking issue type prediction empirical study

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