Effective assignment and assistance to software developers and reviewers

Human reliance and dominance are ubiquitous in sustaining a high-quality large software system. Automatically assigning the right solution providers to the maintenance task at hand is arguably as important as providing the right tool support for it, especially in the far too commonly found state of inadequate or obsolete documentation of large-scale software systems. Two maintenance tasks related to assignment and assistance to software developers and reviewers are addressed, and solutions are proposed. The key insight behind these proposed solutions is the analysis and use of micro-levels of human-to-code and human-to-human interactions (eg., code review). We analyzed code reviews that are managed by Gerrit and found different markers of developer expertise associated with the source code changes and their acceptance, time line, and human roles and feedback involved in the reviews. We formed a developer-expertise model from these markers and showed its application in bug triaging. Specifically, we derived a developer recommendation approach for an incoming change request, named rDevX , from this expertise model. Additionally, we present an approach, namely cHRev, to automatically recommend reviewers who are best suited to participate in a given review, based on their historical contributions as demonstrated in their prior reviews. Furthermore, a comparative study on other previous approaches for developer recommendation and reviewer recommendation was performed. The metrics recall and MRR were used to measure their quantitative effectiveness. Results show that the proposed approaches outperform the subjected competitors with statistical significance.