  Just-in-time defect prediction assigns a defect risk to each new change to a
software repository in order to prioritize review and testing efforts. Over the
last decades different approaches were proposed in literature to craft more
accurate prediction models. However, defect prediction is still not widely used
in industry, due to predictions with varying performance. In this study, we
evaluate existing features on six open-source projects and propose two new
features sets, not yet discussed in literature. By combining all feature sets,
we improve MCC by on average 21%, leading to the best performing models when
compared to state-of-the-art approaches. We also evaluate effort-awareness and
find that on average 14% more defects can be identified, inspecting 20% of
changed lines.
