| 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 | 58* | 2018 |
| 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 | 54 | 2020 |
| 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 | 38 | 2021 |
| “Bad smells” in software analytics papers MS R Krishna, S Majumder, T Menzies Information and Software Technology 112, 35-47, 2019 | 29* | 2019 |
| Revisiting process versus product metrics: A large scale analysis S Majumder, P Mody, T Menzies Empirical Software Engineering, 2020 | 11 | 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 | 9 | 2019 |
| Fair enough: Searching for sufficient measures of fairness S Majumder, J Chakraborty, GR Bai, KT Stolee, T Menzies arXiv preprint arXiv:2110.13029, 2021 | 7 | 2021 |
| 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 | 6 | 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, 2021 | 5 | 2021 |
| 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 | 4 | 2022 |
| Can we achieve fairness using semi-supervised learning? J Chakraborty, H Tu, S Majumder, T Menzies arXiv preprint arXiv:2111.02038, 2021 | 4 | 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 | 2 | 2022 |
| A baseline revisited: Pushing the limits of multi-segment models for context-aware translation S Majumde, S Lauly, M Nadejde, M Federico, G Dinu arXiv preprint arXiv:2210.10906, 2022 | 1 | 2022 |
| 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 |
| When Less is More: On the Value of" Co-training" for Semi-Supervised Software Defect Predictors S Majumder, J Chakraborty, T Menzies arXiv preprint arXiv:2211.05920, 2022 | | 2022 |
| How to GENERALize Across Many Software Projects?(with case studies on Predicting Defect and Project Health) S Majumder, T Xia, R Krishna, T Menzies MSR '22: 19th International Conference on Mining Software Repositories …, 2022 | | 2022 |
| 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 | | 2021 |
| 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 | | 2020 |
Tim MenziesNorth Carolina State University, Computer Science, IEEE FellowVerified email at ncsu.edu
