| Data balancing improves self-admitted technical debt detection M Sridharan, M Mantyla, L Rantala, M Claes 2021 IEEE/ACM 18th International Conference on Mining Software Repositories …, 2021 | 13 | 2021 |
| Predicting technical debt from commit contents: reproduction and extension with automated feature selection L Rantala, M Mäntylä Software Quality Journal 28, 1551-1579, 2020 | 11 | 2020 |
| Prevalence, contents and automatic detection of KL-SATD L Rantala, M Mäntylä, D Lo 2020 46th Euromicro Conference on Software Engineering and Advanced …, 2020 | 11 | 2020 |
| Towards better technical debt detection with NLP and machine learning methods L Rantala Proceedings of the ACM/IEEE 42nd International Conference on Software …, 2020 | 8 | 2020 |
| SoCCMiner: a source code-comments and comment-context miner M Sridharan, M Mäntylä, M Claes, L Rantala Proceedings of the 19th International Conference on Mining Software …, 2022 | 6 | 2022 |
| Pentacet data-23 million contextual code comments and 500,000 satd comments M Sridharan, L Rantala, M Mäntylä, M Claes M3S, University of Oulu, Oulu, Finland, 2023 | 2 | 2023 |
| PENTACET data-23 Million Contextual Code Comments and 250,000 SATD comments M Sridharan, L Rantala, M Mäntylä 2023 IEEE/ACM 20th International Conference on Mining Software Repositories …, 2023 | 1 | 2023 |
| Keyword-labeled self-admitted technical debt and static code analysis have significant relationship but limited overlap L Rantala, M Mäntylä, V Lenarduzzi Software Quality Journal 32 (2), 391-429, 2024 | | 2024 |
| Developing an aspect-based sentiment lexicon for software engineering L Rantala L. Rantala, 2018 | | 2018 |
| Relationship between Self-Admitted Technical Debt and Code-level Technical Debt. An Empirical Evaluation L Rantala, V Lenarduzzi, MV Mäntylä | | |
Mika MäntyläProfessor, University of Helsinki / University of Oulu, Finland在 helsinki.fi 的电子邮件经过验证
