A data-driven approach for understanding invalid bug reports: An industrial case study

Context: Bug reports created during software development and maintenance do not always describe deviations from a system's valid behavior. Such invalid bug reports may consume significant resources and adversely affect the prioritization and resolution of valid bug reports. There is a need to identify preventive actions to reduce the inflow of invalid bug reports. Existing research has shown that manually analyzing invalid bug report descriptions provides cues regarding preventive actions. However, such a manual approach is not cost-effective due to the time required to analyze a sufficiently large number of bug reports needed to identify useful patterns. Furthermore, the analysis needs to be repeated as the underlying causes of invalid bug reports change over time.Objective: In this study, we propose and evaluate the use of Latent Dirichlet Allocation (LDA), a topic modeling approach, to support practitioners in suggesting preventive actions to avoid the creation of similar invalid bug reports in the future. Method: In an industrial case study, we first manually analyzed descriptions of invalid bug reports to identify common patterns in their descriptions. We further investigated to what extent LDA can support this manual process. We used expert-based validation to evaluate the relevance of identified common patterns and their usefulness in suggesting preventive measures. Results: We found that invalid bug reports have common patterns that are perceived as relevant, and they can be used to devise preventive measures. Furthermore, the identification of common patterns can be supported with automation.Conclusion: Using LDA, practitioners can effectively identify representative groups of bug reports (i.e., relevant common patterns) from a large number of bug reports and analyze them further to devise preventive measures.