Suvodeep Majumder

Majumder, S., Chakraborty, J., & Menzies, T. (2024). When less is more: on the value of "co-training" for semi-supervised software defect predictors. EMPIRICAL SOFTWARE ENGINEERING, 29(2). https://doi.org/10.1007/s10664-023-10418-4

Majumder, R., & Reich, B. J. (2023). A deep learning synthetic likelihood approximation of a non-stationary spatial model for extreme streamflow forecasting. SPATIAL STATISTICS, 55. https://doi.org/10.1016/j.spasta.2023.100755

Majumder, S., Chakraborty, J., Bai, G. R., Stolee, K. T., & Menzies, T. (2023). Fair Enough: Searching for Sufficient Measures of Fairness. ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY, 32(6). https://doi.org/10.1145/3585006

Chakraborty, J., Majumder, S., & Tu, H. (2022). Fair-SSL: Building fair ML Software with less data. 2022 IEEE/ACM INTERNATIONAL WORKSHOP ON EQUITABLE DATA & TECHNOLOGY (FAIRWARE 2022), pp. 1–8. https://doi.org/10.1145/3524491.3527305

Majumder, S., Xia, T., Krishna, R., & Menzies, T. (2022). Methods for Stabilizing Models Across Large Samples of Projects (with case studies on Predicting Defect and Project Health). 2022 MINING SOFTWARE REPOSITORIES CONFERENCE (MSR 2022), pp. 566–578. https://doi.org/10.1145/3524842.3527934

Majumder, S., Mody, P., & Menzies, T. (2022). Revisiting process versus product metrics: a large scale analysis. EMPIRICAL SOFTWARE ENGINEERING, 27(3). https://doi.org/10.1007/s10664-021-10068-4

Chakraborty, J., Majumder, S., & Menzies, T. (2021). Bias in Machine Learning Software: Why? How? What to Do? PROCEEDINGS OF THE 29TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING (ESEC/FSE '21), pp. 429–440. https://doi.org/10.1145/3468264.3468537

Shrikanth, N. C., Majumder, S., & Menzies, T. (2021). Early Life Cycle Software Defect Prediction. Why? How? 2021 IEEE/ACM 43RD INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE 2021), pp. 448–459. https://doi.org/10.1109/ICSE43902.2021.00050