Assigning appropriate priority to bugs is critical for timely addressing important software maintenance issues. An underlying aspect is the effectiveness of assigning priorities: if the priorities of a fair number of bugs are changed, it indicates delays in fixing critical bugs. There has been little prior work on understanding the dynamics of changing bug priorities. In this paper, we performed an empirical study to observe and understand the changes in bugs' priority to build a 3-W model on Why and When bug priorities change, and Who performs the change. We conducted interviews and a survey with practitioners as well as performed a quantitative analysis containing 225,000 bug reports, developers' comments, and source code changes from 24 open-source systems. The interviews with 11 developers from industry aim to establish an initial model to characterize the changes in bugs priority. The survey with an additional 38 developers was to understand their experience in why and when bug priorities change, and who performs the change. Then, we conducted a manual inspection of the collected data on open-source projects to compare our final bugs priority change model with changes identified in practice. Our quantitative results confirmed the outcomes of our interviews and surveys. For instance, we observed frequent changes in bug priorities and their impact on delaying critical bug fixes especially just before shipping a new release. Our findings can enable 1) researchers to build automated tools for checking and validating requests for bug priority changes, 2) practitioners to use a standard format in documenting and approving bug priority changes, and 3) educators to teach the better management of bug priorities.

Developers may follow a tedious process to find the cause of a bug based on code reviews and reproducing the abnormal behavior. In this paper, we propose an automated approach to finding and ranking potential classes with the respect to the probability of containing a bug based on a bug report description. Our approach finds a good balance between minimizing the number of recommended classes and maximizing the relevance of the proposed solution using a multi-objective optimization algorithm. The relevance of the recommended classes (solution) is estimated based on the use of the history of changes and bug-fixing, and the lexical similarity between the bug report description and the API documentation. We evaluated our system on 6 open source Java projects, using the version of the project before fixing the bug of many bug reports. The experimental results show that the search-based approach significantly outperforms three state-of-the-art methods in recommending relevant files for bug reports. In particular, our multi-objective approach is able to successfully locate the true buggy methods within the top 10 recommendations for over 87% of the bug reports.