demonstration

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

Authors:

Shanping LiAuthors Info & Claims

ICSE '18: Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings

Pages 9 - 12

https://doi.org/10.1145/3183440.3183478

Published: 27 May 2018 Publication History

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

References

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Qiao Huang, Emad Shihab, Xin Xia, David Lo, and Shanping Li. 2017. Identifying self-admitted technical debt in open source projects using text mining. EMSE (2017).

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Erin Lim, Nitin Taksande, and Carolyn Seaman. 2012. A balancing act: what software practitioners have to say about technical debt. IEEE software (2012).

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Everton Maldonado, Emad Shihab, and Nikolaos Tsantalis. 2017. Using natural language processing to automatically detect self-admitted technical debt. TSE (2017).

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Everton da S Maldonado and Emad Shihab. 2015. Detecting and quantifying different types of self-admitted technical debt. In MTD.

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Xin Xia, David Lo, Denzil Correa, Ashish Sureka, and Emad Shihab. 2016. It takes two to tango: Deleted stack overflow question prediction with text and meta features. In COMPSAC.

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Xin Xia, David Lo, Emad Shihab, Xinyu Wang, and Bo Zhou. 2015. Automatic, high accuracy prediction of reopened bugs. Automated Software Engineering (2015).

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Xin Xia, David Lo, Xinyu Wang, and Xiaohu Yang. 2015. Who should review this change?: Putting text and file location analyses together for more accurate recommendations. In ICSME.

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Cited By

OBrien DBiswas SImtiaz SAbdalkareem RShihab ERajan HRoychoudhury APaiva AAbreu RStorey M(2024)Are Prompt Engineering and TODO Comments Friends or Foes? An Evaluation on GitHub CopilotProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3639176(1-13)Online publication date: 20-May-2024
https://dl.acm.org/doi/10.1145/3597503.3639176
Xiao TZeng ZWang DHata HMcIntosh SMatsumoto K(2024)Quantifying and characterizing clones of self-admitted technical debt in build systemsEmpirical Software Engineering10.1007/s10664-024-10449-529:2Online publication date: 26-Feb-2024
https://dl.acm.org/doi/10.1007/s10664-024-10449-5
Lin CJiang BHuang QWang Y(2024)An Empirical Study on the Urgent Self-admitted Technical DebtComputer Supported Cooperative Work and Social Computing10.1007/978-981-99-9640-7_23(309-320)Online publication date: 5-Jan-2024
https://doi.org/10.1007/978-981-99-9640-7_23
Show More Cited By

Index Terms

SATD detector: a text-mining-based self-admitted technical debt detection tool
1. Software and its engineering
  1. Software notations and tools
    1. Software maintenance tools

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Information & Contributors

Information

Published In

cover image ACM Conferences

ICSE '18: Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings

May 2018

231 pages

ISBN:9781450356633

DOI:10.1145/3183440

Conference Chair:
Michel Chaudron
Chalmers University of Technology, University of Gothenburg, Sweden
,
General Chair:
Ivica Crnkovic
Chalmers University of Technology, University of Gothenburg, Sweden
,
Program Chairs:
Marsha Chechik
University of Toronto, Canada
,
Mark Harman
Facebook and University College London, United Kingdom

Copyright © 2018 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGSOFT: ACM Special Interest Group on Software Engineering
IEEE-CS: Computer Society

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 May 2018

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ICSE '18: 40th International Conference on Software Engineering

May 27 - June 3, 2018

Gothenburg, Sweden

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Overall Acceptance Rate 276 of 1,856 submissions, 15%

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Cited By

OBrien DBiswas SImtiaz SAbdalkareem RShihab ERajan HRoychoudhury APaiva AAbreu RStorey M(2024)Are Prompt Engineering and TODO Comments Friends or Foes? An Evaluation on GitHub CopilotProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3639176(1-13)Online publication date: 20-May-2024
https://dl.acm.org/doi/10.1145/3597503.3639176
Xiao TZeng ZWang DHata HMcIntosh SMatsumoto K(2024)Quantifying and characterizing clones of self-admitted technical debt in build systemsEmpirical Software Engineering10.1007/s10664-024-10449-529:2Online publication date: 26-Feb-2024
https://dl.acm.org/doi/10.1007/s10664-024-10449-5
Lin CJiang BHuang QWang Y(2024)An Empirical Study on the Urgent Self-admitted Technical DebtComputer Supported Cooperative Work and Social Computing10.1007/978-981-99-9640-7_23(309-320)Online publication date: 5-Jan-2024
https://doi.org/10.1007/978-981-99-9640-7_23
Gong LZhang JWei MZhang HHuang Z(2023)What Is the Intended Usage Context of This Model? An Exploratory Study of Pre-Trained Models on Various Model RepositoriesACM Transactions on Software Engineering and Methodology10.1145/356993432:3(1-57)Online publication date: 3-May-2023
https://dl.acm.org/doi/10.1145/3569934
G SR P(2023)Automated Identification and Prioritization of Self-Admitted Technical Debt Using NLP Word Embeddings2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS)10.1109/ICSSAS57918.2023.10331839(963-971)Online publication date: 18-Oct-2023
https://doi.org/10.1109/ICSSAS57918.2023.10331839
Li YSoliman MAvgeriou PVan Ittersum M(2023)DebtViz: A Tool for Identifying, Measuring, Visualizing, and Monitoring Self-Admitted Technical Debt2023 IEEE International Conference on Software Maintenance and Evolution (ICSME)10.1109/ICSME58846.2023.00072(558-562)Online publication date: 1-Oct-2023
https://doi.org/10.1109/ICSME58846.2023.00072
Rantala LMäntylä MLenarduzzi V(2023)Keyword-labeled self-admitted technical debt and static code analysis have significant relationship but limited overlapSoftware Quality Journal10.1007/s11219-023-09655-z32:2(391-429)Online publication date: 16-Nov-2023
https://dl.acm.org/doi/10.1007/s11219-023-09655-z
Di Salle ARota ANguyen PDi Ruscio DFontana FSala IErnst NLenarduzzi VSharma T(2022)PILOTProceedings of the International Conference on Technical Debt10.1145/3524843.3528093(41-45)Online publication date: 16-May-2022
https://dl.acm.org/doi/10.1145/3524843.3528093
Russo BCamilli MMock MLo DMcIntosh SNovielli N(2022)WeakSATDProceedings of the 19th International Conference on Mining Software Repositories10.1145/3524842.3528469(448-453)Online publication date: 23-May-2022
https://dl.acm.org/doi/10.1145/3524842.3528469
Sridharan MMäntylä MClaes MRantala LLo DMcIntosh SNovielli N(2022)SoCCMinerProceedings of the 19th International Conference on Mining Software Repositories10.1145/3524842.3527998(242-246)Online publication date: 23-May-2022
https://dl.acm.org/doi/10.1145/3524842.3527998
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