Identifying and predicting key features to support bug reporting

• Published in: Journal of software : evolution and process Vol. 31; no. 12
• Main Authors: Karim, Md. Rejaul, Ihara, Akinori, Choi, Eunjong, Iida, Hajimu
• Format: Journal Article
• Language: English
• Published: Chichester Wiley Subscription Services, Inc 01-12-2019
• Subjects: bug report; Classification; Comparative studies; Debugging; Evaluation; Fixing; Open source software; open‐source projects; prediction models
• ISSN: 2047-7473; 2047-7481
• DOI: 10.1002/smr.2184

Bug reports are the primary means through which developers triage and fix bugs. To achieve this effectively, bug reports need to clearly describe those features that are important for the developers. However, previous studies have found that reporters do not always provide such features. Therefore, we first perform an exploratory study to identify the key features that reporters frequently miss in their initial bug report submissions. Then, we propose an approach that predicts whether reporters should provide certain key features to ensure a good bug report. A case study of the bug reports for Camel, Derby, Wicket, Firefox, and Thunderbird projects shows that Steps to Reproduce, Test Case, Code Example, Stack Trace, and Expected Behavior are the additional features that reporters most often omit from their initial bug report submissions. We also find that these features significantly affect the bug‐fixing process. On the basis of our findings, we build and evaluate classification models using four different text‐classification techniques to predict key features by leveraging historical bug‐fixing knowledge. The evaluation results show that our models can effectively predict the key features. Our comparative study of different text‐classification techniques shows that naïve Bayes multinomial (NBM) outperforms other techniques. Our findings can benefit reporters to improve the contents of bug reports.
In this research, we perform an exploratory study using quantitative and qualitative methods on five open source projects for identifying and predicting key features to support bug reporting. Through qualitative analysis, we identify five key features, which significantly affect bug‐fixing process if the reporters miss to provide them in the initial bug reports. To support reporters, we build classification models to predict key features by leveraging historical bug‐fixing knowledge and our models achieve promising F1‐scores.