Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
6-2018
Abstract
In software projects, technical debt metaphor is used to describe the situation where developers and managers have to accept compromises in long-Term software quality to achieve short-Term goals. There are many types of technical debt, and self-Admitted technical debt (SATD) was proposed recently to consider debt that is introduced intentionally (e.g., through temporaryfi x) and admitted by developers themselves. Previous work has shown that SATD can be successfully detected using source code comments. However, most current state-of-The-Art approaches identify SATD comments through pattern matching, which achieve high precision but very low recall. That means they may miss many SATD comments and are not practical enough. In this paper, we propose SATD Detector, a tool that is able to (i) automatically detect SATD comments using text mining and (ii) highlight, list and manage detected comments in an integrated development environment (IDE). This tool consists of a Java library and an Eclipse plug-in. The Java library is the back-end, which provides command-line interfaces and Java APIs to re-Train the text mining model using users' data and automatically detect SATD comments using either the build-in model or a user-specified model. The Eclipse plug-in, which is the front-end, first leverages our pre-Trained composite classifier to detect SATD comments, and then highlights and marks these detected comments in the source code editor of Eclipse. In addition, the Eclipse plug-in provides a view in IDE which collects all detected comments for management. Demo URL: https://youtu.be/sn4gU2qhGm0 Java library download: https://git.io/vNdnY Eclipse plug-in download: https://goo.gl/ZzjBzp.
Keywords
Eclipse plug-in, SATD detection, Self-admitted technical debt
Discipline
Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
ICSE 2018: Proceedings of the 40th ACM/IEEE International Conference on Software Engineering: Gothenburg, Sweden, May 27 - June 3
First Page
9
Last Page
12
ISBN
9781450356633
Identifier
10.1145/3183440.3183478
Publisher
ACM
City or Country
New York
Citation
LIU, Zhongxin; HUANG, Qiao; XIA, Xin; SHIHAB, Emad; LO, David; and LI, Shanping.
SATD detector: A text-mining-based self-admitted technical debt detection tool. (2018). ICSE 2018: Proceedings of the 40th ACM/IEEE International Conference on Software Engineering: Gothenburg, Sweden, May 27 - June 3. 9-12.
Available at: https://ink.library.smu.edu.sg/sis_research/4104
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3183440.3183478