Leevi Rantala. 2020. Towards better technical debt detection with NLP and machine learning methods. In Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Companion Proceedings (ICSE '20). Association for Computing Machinery, New York, NY, USA, 242–245. DOI:https://doi.org/10.1145/3377812.3381404
Towards better technical debt detection with NLP and machine learning methods
1University of Oulu, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 0.4 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe202103177626
Association for Computing Machinery,
|Publish Date:|| 2021-03-17
Technical debt (TD) is an economical term used to depict non-optimal choices made in the software development process. It occurs usually when developers take shortcuts instead of following agreed upon development practices, and unchecked growth of technical debt can start to incur negative effects for software development processes.
Technical debt detection and management is mainly done manually, and this is both slow and costly way of detecting technical debt. Automatic detection would solve this issue, but even state-of-the-art tools of today do not accurately detect the appearance of technical debt. Therefore, increasing the accuracy of automatic classification is of high importance, so that we could eliminate significant portion from the costs relating to technical debt detection.
This research aims to solve the problem in detection accuracy by bringing in together static code analysis and natural language processing. This combination of techniques will allow more accurate detection of technical debt, when compared to them being used separately from each other. Research also aims to discover themes and topics from written developer messages that can be linked to technical debt. These can help us to understand technical debt from developers’ viewpoint. Finally, we will build an open-source tool/plugin that can be used to accurately detect technical debt using both static analysis and natural language processing methods.
|Pages:||242 - 245|
42nd ACM/IEEE International Conference on Software Engineering, ICSE-Companion 2020, 27 June-19 July 2020 Seoul, South Korea
ACM/IEEE International Conference on Software Engineering
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
113 Computer and information sciences
© 2019 Association for Computing Machinery. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in 42nd ACM/IEEE International Conference on Software Engineering, ICSE-Companion 2020, 27 June-19 July 2020 Seoul, South Korea, https://doi.org/10.1145/3377812.3381404.