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Suvodeep Majumder
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Year
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
1642021
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
1122020
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
83*2018
“Bad smells” in software analytics papers
MS R Krishna, S Majumder, T Menzies
Information and Software Technology 112, 35-47, 2019
37*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
252021
Revisiting process versus product metrics: A large scale analysis
S Majumder, P Mody, T Menzies
Empirical Software Engineering, 2020
232020
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
182023
Why software projects need heroes (lessons learned from 1100+ projects)
S Majumder, J Chakraborty, A Agrawal, T Menzies
arXiv preprint arXiv:1904.09954, 2019
132019
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
112022
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
102021
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
82022
Can we achieve fairness using semi-supervised learning?
J Chakraborty, H Tu, S Majumder, T Menzies
arXiv preprint arXiv:2111.02038, 2021
82021
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
42022
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), 1-33, 2024
22024
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
22021
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
22020
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
22018
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
On the Exploitation of Repeated Structures for Software Analytics
S Majumder
North Carolina State University, 2023
2023
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