Cold-Start Software Analytics

Abstract

Software project artifacts such as source code, requirements, and change logs represent a gold-mine of actionable information. As a result, software analytic solutions have been developed to mine repositories and answer questions such as "who is the expert?,'' "which classes are fault prone?,'' or even "who are the domain experts for these fault-prone classes?'' Analytics often require training and configuring in order to maximize performance within the context of each project. A cold-start problem exists when a function is applied within a project context without first configuring the analytic functions on project-specific data. This scenario exists because of the non-trivial effort necessary to instrument a project environment with candidate tools and algorithms and to empirically evaluate alternate configurations. We address the cold-start problem by comparatively evaluating `best-of-breed' and `profile-driven' solutions, both of which reuse known configurations in new project contexts. We describe and evaluate our approach against 20 project datasets for the three analytic areas of artifact connectivity, fault-prediction, and finding the expert, and show that the best-of-breed approach outperformed the profile-driven approach in all three areas; however, while it delivered acceptable results for artifact connectivity and find the expert, both techniques underperformed for cold-start fault prediction.