Supatsara WattanakriengkraiRungroj MaipraditHideki HataMorakot ChoetkiertikulThanwadee SunetnantaKenichi MatsumotoNara Institute of Science and TechnologyMahidol University2020-01-272020-01-272019-03-05Proceedings - 2018 9th International Workshop on Empirical Software Engineering in Practice, IWESEP 2018. (2019), 7-122-s2.0-85063955510https://repository.li.mahidol.ac.th/handle/20.500.14594/50638© 2018 IEEE. In software projects, technical debt takes place when a developer adopting a trivial solution containing quick and easy shortcuts to implement over a suitable solution that can take a longer time to solve a problem. This can cause major additional costs leading to negative impacts for software maintenance since those shortcuts might need to be reworked in the future. Detecting technical debt early can help a team cope with those risks. In this paper, we focus on Self-Admitted Technical Debt (SATD) that is a debt intentionally produced by developers. We propose an automated model to identify two most common types of self-admitted technical debt, requirement and design debt, from source code comments. We combine N-gram IDF and auto-sklearn machine learning to build the model. With the empirical evaluation on ten projects, our approach outperform the baseline method by improving the performance over 20% when identifying requirement self-admitted technical debt and achieving an average F1-score of 64% when identifying design self-admitted technical debt.Mahidol UniversityComputer ScienceConference PaperSCOPUS10.1109/IWESEP.2018.00010