Publication Type

Journal Article

Version

acceptedVersion

Publication Date

3-2019

Abstract

Technical debt is a metaphor to reflect the tradeoff software engineers make between short term benefitsand long term stability. Self-admitted technical debt (SATD), a variant of technical debt, has been proposed to identify debt that is intentionally introduced during software development, e.g., temporary fixes and workarounds. Previous studies have leveraged human-summarized patterns (which represent n-gram phrases that can be used to identify SATD) or text mining techniques to detect SATD in source code comments. However, several characteristics of SATD features in code comments, such as vocabulary diversity, project uniqueness, length and semantic variations, pose a big challenge to the accuracy of pattern or traditional text-mining based SATD detection, especially for cross-project deployment. Furthermore, although traditional text-mining based method outperforms pattern-based method in prediction accuracy, the text features it uses are less intuitive than human-summarized patterns, which makes the prediction results hard to explain. To improve the accuracy of SATD prediction, especially for cross-project prediction, we propose a Convolutional Neural Network (CNN)-based approach for classifying code comments as SATD or non-SATD. To improve the explainability of our model’s prediction results, we exploit the computational structure of CNNs to identify key phrases and patterns in code comments that are most relevant to SATD. We have conducted an extensive set of experiments with 62,566 code comments from 10 open-source projects and a user study with 150 comments of another three projects. Our evaluation confirms the effectiveness of different aspects of our approach and its superior performance, generalizability, adaptability and explainability over current state-of-the-art traditional text-mining based methods for SATD classification.

Keywords

Self-admitted technical debt, Convolutional Neural Network, Cross project prediction, Model explainability, Model generalizability, Model adaptability

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

ACM Transactions on Software Engineering and Methodology

Volume

28

Issue

3

First Page

1

Last Page

46

ISSN

1049-331X

Identifier

10.1145/3324916

Publisher

Association for Computing Machinery (ACM)

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1145/3324916

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