Transactions on Software Engineering

The IEEE Transactions on Software Engineering (TSE) is an archival journal published bimonthly. We are interested in well-defined theoretical results and empirical studies that have potential impact on the construction, analysis, or management of software. Read the full scope of TSE.

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From the November 2017 Issue

Using Natural Language Processing to Automatically Detect Self-Admitted Technical Debt

By Everton da Silva Maldonado, Emad Shihab, and Nikolaos Tsantalis

Featured article thumbnail image The metaphor of technical debt was introduced to express the trade off between productivity and quality, i.e., when developers take shortcuts or perform quick hacks. More recently, our work has shown that it is possible to detect technical debt using source code comments (i.e., self-admitted technical debt), and that the most common types of self-admitted technical debt are design and requirement debt. However, all approaches thus far heavily depend on the manual classification of source code comments. In this paper, we present an approach to automatically identify design and requirement self-admitted technical debt using Natural Language Processing (NLP). We study 10 open source projects: Ant, ArgoUML, Columba, EMF, Hibernate, JEdit, JFreeChart, JMeter, JRuby and SQuirrel SQL and find that 1) we are able to accurately identify self-admitted technical debt, significantly outperforming the current state-of-the-art based on fixed keywords and phrases; 2) words related to sloppy code or mediocre source code quality are the best indicators of design debt, whereas words related to the need to complete a partially implemented requirement in the future are the best indicators of requirement debt; and 3) we can achieve 90 percent of the best classification performance, using as little as 23 percent of the comments for both design and requirement self-admitted technical debt, and 80 percent of the best performance, using as little as 9 and 5 percent of the comments for design and requirement self-admitted technical debt, respectively. The last finding shows that the proposed approach can achieve a good accuracy even with a relatively small training dataset.

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Editorials and Announcements


  • We are pleased to announce that Nenad Medvidović, a Professor in the Computer Science Department and in the Informatics Program at the University of Southern California, has been selected as the new Editor-in-Chief of the IEEE Transactions on Software Engineering starting in 2018.
  • According to Clarivate Analytics' 2016 Journal Citation Report, TSE has an impact factor of 3.272.


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