SATD detector: a text-mining-based self-Admitted technical debt detection tool

Zhongxin Liu, Qiao Huang, Xin Xia, Emad Shihab, David Lo, Shanping Li

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

4 Citations (Scopus)

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. 

Original languageEnglish
Title of host publicationProceedings - 2018 ACM/IEEE 40th International Conference on Software Engineering: Companion Proceeedings
Subtitle of host publicationICSE-Companion 2018
EditorsIvica Crnkovic
Place of PublicationNew York NY USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages9-12
Number of pages4
ISBN (Electronic)9781450356633
DOIs
Publication statusPublished - 2018
EventInternational Conference on Software Engineering 2018 - Gothenburg, Sweden
Duration: 27 May 20183 Jun 2018
Conference number: 40th
https://www.icse2018.org/

Conference

ConferenceInternational Conference on Software Engineering 2018
Abbreviated titleICSE 2018
CountrySweden
CityGothenburg
Period27/05/183/06/18
Internet address

Keywords

  • Eclipse plug-in
  • SATD detection
  • Self-Admitted technical debt

Cite this

Liu, Z., Huang, Q., Xia, X., Shihab, E., Lo, D., & Li, S. (2018). SATD detector: a text-mining-based self-Admitted technical debt detection tool. In I. Crnkovic (Ed.), Proceedings - 2018 ACM/IEEE 40th International Conference on Software Engineering: Companion Proceeedings : ICSE-Companion 2018 (pp. 9-12). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1145/3183440.3183478