Bug report assignee recommendation using activity profiles

One question which frequently arises within the context of artifacts stored in a bug tracking repository is: “who should work on this bug report?” A number of approaches exist to semi-automatically identify and recommend developers, e.g. using machine learning techniques and social networking analysis. In this work, we propose a new approach for assignee recommendation leveraging user activities in a bug tracking repository. Within the bug tracking repository, an activity profile is created for each user from the history of all his activities (i.e. review, assign, and resolve). This profile, to some extent, indicates the user's role, expertise, and involvement in this project. These activities influence and contribute to the identification and ranking of suitable assignees. In order to evaluate our work, we apply it to bug reports of three different projects. Our results indicate that the proposed approach is able to achieve an average hit ratio of 88%. Comparing this result to the LDA-SVM - based assignee recommendation technique, it was found that the proposed approach performs better.