Dealing with imbalanced data for interpretable defect prediction

Context Interpretation has been considered as a key factor to apply defect prediction in practice. As interpretation from rule-based interpretable models can provide insights about past defects with high quality, many prior studies attempt to construct interpretable models for both accurate prediction and comprehensible interpretation. However, class imbalance is usually ignored, which may bring huge negative impact on interpretation. Objective In this paper, we are going to investigate resampling techniques, a popular solution to deal with imbalanced data, on interpretation for interpretable models. We also investigate the feasibility to construct interpretable defect prediction models directly on original data. Further, we are going to propose a rule-based interpretable model which can deal with imbalanced data directly. Method We conduct an empirical study on 47 publicly available datasets to investigate the impact of resampling techniques on rule-based interpretable models and the feasibility to construct such models directly on original data. We also improve gain function and tolerate lower confidence based on rule induction algorithms to deal with imbalanced data. Results We find that (1) resampling techniques impact on interpretable models heavily from both feature importance and model complexity, (2) it is not feasible to construct meaningful interpretable models on original but imbalanced data due to low coverage of defects and poor performance, and (3) our proposed approach is effective to deal with imbalanced data compared with other rule-based models. Conclusion Imbalanced data heavily impacts on the interpretable defect prediction models. Resampling techniques tend to shift the learned concept, while constructing rule-based interpretable models on original data may also be infeasible. Thus, it is necessary to construct rule-based models which can deal with imbalanced data well in further studies.