Publication Type

Journal Article

Version

acceptedVersion

Publication Date

12-2019

Abstract

Self-Admitted Technical Debt (SATD) refers to technical debt that is introduced intentionally. Previous studies that identify SATD at the file-level in isolation cannot describe the TD context related to multiple files. Therefore, it is more beneficial to identify the SATD once a change is being made. We refer to this type of TD identification as “Change-level SATD Determination”, and identifying SATD at the change-level can help to manage and control TD by understanding the TD context through tracing the introducing changes. In this paper, we propose a change-level SATD Determination mode by extracting 25 features from software changes that are divided into three dimensions, namely diffusion, history and message, respectively. To evaluate the effectiveness of our proposed model, we perform an empirical study on 7 open source projects containing a total of 100,011 software changes. The experimental results show that our model achieves a promising and better performance than four baselines in terms of AUC and cost-effectiveness. On average across the 7 experimental projects, our model achieves AUC of 0.82, cost-effectiveness of 0.80, which is a significant improvement over the comparison baselines used. In addition, we found that “Diffusion” is the most discriminative dimension for determining TD-introducing changes

Keywords

Self-admitted Technical Debt, Software Change, Labeling, Measurement, Software quality, Technical Debt, Feature extraction, Change-level Determination

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

IEEE Transactions on Software Engineering

Volume

45

Issue

12

First Page

1211

Last Page

1229

ISSN

0098-5589

Identifier

10.1109/TSE.2018.2831232

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TSE.2018.2831232

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