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Suvodeep Majumder
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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
542020
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
382021
“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
112020
Why software projects need heroes (lessons learned from 1100+ projects)
S Majumder, J Chakraborty, A Agrawal, T Menzies
arXiv preprint arXiv:1904.09954, 2019
92019
Fair enough: Searching for sufficient measures of fairness
S Majumder, J Chakraborty, GR Bai, KT Stolee, T Menzies
arXiv preprint arXiv:2110.13029, 2021
72021
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
62021
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
52021
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
42022
Can we achieve fairness using semi-supervised learning?
J Chakraborty, H Tu, S Majumder, T Menzies
arXiv preprint arXiv:2111.02038, 2021
42021
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
22022
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
12022
Fairway: SE principles for building fairer software
J Chakraborty, S Majumder, Z Wu, T Menzies
arXiv preprint arXiv:2003.10354, 2020
12020
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
12019
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
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