| Bias in machine learning software: Why? how? what to do? J Chakraborty, S Majumder, T Menzies Proceedings of the 29th ACM joint meeting on european software engineering …, 2021 | 196 | 2021 |
| Fairway: a way to build fair ML software J Chakraborty, S Majumder, Z Yu, T Menzies Proceedings of the 28th ACM joint meeting on European software engineering …, 2020 | 127 | 2020 |
| 500+ times faster than deep learning:(a case study exploring faster methods for text mining stackoverflow) S Majumder, N Balaji, K Brey, W Fu, T Menzies 2018 IEEE/ACM 15th International Conference on Mining Software Repositories …, 2018 | 88* | 2018 |
| “Bad smells” in software analytics papers MS R Krishna, S Majumder, T Menzies Information and Software Technology 112, 35-47, 2019 | 38* | 2019 |
| Early life cycle software defect prediction. why? how? NC Shrikanth, S Majumder, T Menzies 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE …, 2021 | 30 | 2021 |
| Fair enough: Searching for sufficient measures of fairness S Majumder, J Chakraborty, GR Bai, KT Stolee, T Menzies ACM Transactions on Software Engineering and Methodology 32 (6), 1-22, 2023 | 25 | 2023 |
| Revisiting process versus product metrics: A large scale analysis S Majumder, P Mody, T Menzies Empirical Software Engineering, 2020 | 25 | 2020 |
| Why software projects need heroes (lessons learned from 1100+ projects) S Majumder, J Chakraborty, A Agrawal, T Menzies arXiv preprint arXiv:1904.09954, 2019 | 13 | 2019 |
| A baseline revisited: Pushing the limits of multi-segment models for context-aware translation S Majumder, S Lauly, M Nadejde, M Federico, G Dinu arXiv preprint arXiv:2210.10906, 2022 | 12 | 2022 |
| Fair-SSL: Building fair ML Software with less data J Chakraborty, S Majumder, H Tu Proceedings of the 2nd International Workshop on Equitable Data and …, 2022 | 10 | 2022 |
| Can we achieve fairness using semi-supervised learning? J Chakraborty, H Tu, S Majumder, T Menzies arXiv preprint arXiv:2111.02038, 2021 | 10 | 2021 |
| Early life cycle software defect prediction. why? how?. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) NC Shrikanth, S Majumder, T Menzies IEEE Computer Society, Los Alamitos, CA, USA, 448-459, 2021 | 9 | 2021 |
| Methods for stabilizing models across large samples of projects (with case studies on predicting defect and project health) S Majumder, T Xia, R Krishna, T Menzies Proceedings of the 19th International Conference on Mining Software …, 2022 | 4 | 2022 |
| When less is more: on the value of “co-training” for semi-supervised software defect predictors S Majumder, J Chakraborty, T Menzies Empirical Software Engineering 29 (2), 51, 2024 | 3 | 2024 |
| Fairway: A Way to Build Fair ML Software (ESEC/FSE 2020). Association for Computing Machinery, New York, NY, USA, 654–665 J Chakraborty, S Majumder, Z Yu, T Menzies | 3 | 2020 |
| Bias in Machine Learning Software: Why? How? What to Do?(ESEC/FSE 2021). Association for Computing Machinery, New York, NY, USA, 429–440 J Chakraborty, S Majumder, T Menzies | 2 | 2021 |
| 500+ times faster than deep learning S Majumder, N Balaji, K Brey, W Fu, T Menzies Proceedings of the 15th International Conference on Mining Software …, 2018 | 2 | 2018 |
| Fairway: SE principles for building fairer software J Chakraborty, S Majumder, Z Wu, T Menzies arXiv preprint arXiv:2003.10354, 2020 | 1 | 2020 |
| Communication and Code Dependency Effects on Software Code Quality: An Empirical Analysis of Herbsleb Hypothesis S Majumder, J Chakraborty, A Agrawal, T Menzies arXiv preprint arXiv:1904.09954, 2019 | 1 | 2019 |
| On the Exploitation of Repeated Structures for Software Analytics S Majumder North Carolina State University, 2023 | | 2023 |
Tim MenziesNorth Carolina State University, Computer Science, IEEE Fellow, ASE FellowVerified email at ncsu.edu
