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

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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.

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Notes

  1. code:https://github.com/Wxxxxx2023/SATD-code.git

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Acknowledgements

The work reported here was supported by Natural Science Basic Research Program of Shaanxi (Program No.2023-JC-YB-615) and Shaanxi Social Science Fund in China (Program No.2023R102). The authors thank all the participants in the experiment and the professors for their advice. The authors would like to thank the anonymous reviewers for their valuable suggestion and constructive comments.

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Authors and Affiliations

  1. School of Software, Northwestern Polytechnical University, 127 Youyi St. West, Xi’an, 710072, Shannxi, People’s Republic of China

    Ming Yin, Jiaze Wang & Cunzhi Gao

  2. Debbie and Jerry Ivy College of Business, Iowa State University, 2200 Gerdin Building 2167 Union Drive, Ames, IA, USA

    Dan Zhu

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  1. Ming Yin
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Ming Yin and Jiaze Wang contributed equally to this work.

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Yin, M., Wang, J., Zhu, D. et al. A two-stage approach for identifying and interpreting self-admitted technical debt. Appl Intell 53, 26592–26602 (2023). https://doi.org/10.1007/s10489-023-04941-6

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