TY - GEN
T1 - Identifying design and requirement self-admitted technical debt using N-gram IDF
AU - Wattanakriengkrai, Supatsara
AU - Maipradit, Rungroj
AU - Hata, Hideki
AU - Choetkiertikul, Morakot
AU - Sunetnanta, Thanwadee
AU - Matsumoto, Kenichi
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - 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.
AB - 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.
KW - Automated-machine-learning
KW - Comment-classification
KW - N-gram-IDF
KW - Self-admitted-technical-debt
UR - https://www.scopus.com/pages/publications/85063955510
U2 - 10.1109/IWESEP.2018.00010
DO - 10.1109/IWESEP.2018.00010
M3 - Conference contribution
AN - SCOPUS:85063955510
T3 - Proceedings - 2018 9th International Workshop on Empirical Software Engineering in Practice, IWESEP 2018
SP - 7
EP - 12
BT - Proceedings - 2018 9th International Workshop on Empirical Software Engineering in Practice, IWESEP 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th International Workshop on Empirical Software Engineering in Practice, IWESEP 2018
Y2 - 4 December 2018
ER -