A two-stage approach for identifying and interpreting self-admitted technical debt

Ming Yin, Jiaze Wang, Dan Zhu, Cunzhi Gao

Abstract: A major current focus in software quality is how to identify and interpret Self-admitted technical debt(SATD). While many methods have been proposed to identify SATD, these methods are neither interpretable nor generic. There remains a need for an efficient method that can interpret SATD. In this paper, we propose a two-stage approach to identify and interpret SATD using interpretable methods. In the first stage, the decision tree model is combined into an integrated model to identify SATD better. We apply SHAP, LIME, and Anchors models in the second stage to interpret the result. The experiments of 10 projects show that our method not only can effectively detect and explain SATD both in within-project and cross-project experiments, but also has a good explanation for self-generated data outside the dataset.

External IDs:doi:10.1007/s10489-023-04941-6