Visual representation of bug report assignment recommendations

Software development projects typically use an issue tracking system where the project members and users can either report faults or request additional features. Each of these reports needs to be triaged to determine such things as the priority of the report or which developers should be assigned to resolve the report. To assist a triager with report assigning, an assignment recommender has been suggested as a means of improving the process. However, proposed assignment recommenders typically present a list of developer names, without an explanation of the rationale. This work focuses on providing visual explanations for bug report assignment recommendations. We examine the use of a supervised and unsupervised machine learning algorithm for the assignment recommendation from which we can provide recommendation rationale. We explore the use of three types of graphs for the presentation of the rationale and validate their use-cases and usability through a small user study

Recommending expert developers using usage and implementation expertise

Knowing the expert developers of a software development project has great significance in large-scale and geographically distributed projects. However, finding these expert developers can be challenging, which becomes more complicated over time as the development team gets bigger and more distributed. This thesis presents an expert developer recommender system for methods, based on the usage expertise, implementation expertise, and the combination of both, for the developers of a software project. A developer acquires usage expertise on a method by calling or using it and implementation expertise by creating or modifying it. To determine the method expertise of the developers, we mine both the source code repository and the version histories of a software development project. We determine the accuracy of our system by calculating the percentage of successful recommendations within the Top-N results. Through several experiments, we found that our recommender system produces around 90% average accuracy for Top-10 recommendations.Natural Science and Engineering Research Council of Canada (NSERC