184 citations · 409 across the 10 of their papers we have counts for
10 papers
When Less is More: On the Value of "Co-training" for Semi-Supervised Software Defect Predictors
Suvodeep Majumder, Joymallya Chakraborty, Tim Menzies
Labeling a module defective or non-defective is an expensive task. Hence, there are often limits on how much-labeled data is available for training. Semi-supervised classifiers use…
Fair-SSL: Building fair ML Software with less data
Joymallya Chakraborty, Suvodeep Majumder, Huy Tu
Ethical bias in machine learning models has become a matter of concern in the software engineering community. Most of the prior software engineering works concentrated on finding e…
Fair Enough: Searching for Sufficient Measures of Fairness
Suvodeep Majumder, Joymallya Chakraborty, Gina R. Bai +2
Testing machine learning software for ethical bias has become a pressing current concern. In response, recent research has proposed a plethora of new fairness metrics, for example,…
Bias in Machine Learning Software: Why? How? What to do?
Joymallya Chakraborty, Suvodeep Majumder, Tim Menzies
Increasingly, software is making autonomous decisions in case of criminal sentencing, approving credit cards, hiring employees, and so on. Some of these decisions show bias and adv…
Early Life Cycle Software Defect Prediction. Why? How?
N. C. Shrikanth, Suvodeep Majumder, Tim Menzies
Many researchers assume that, for software analytics, "more data is better." We write to show that, at least for learning defect predictors, this may not be true. To demonstrate th…
Revisiting Process versus Product Metrics: a Large Scale Analysis
Suvodeep Majumder, Pranav Mody, Tim Menzies
Numerous methods can build predictive models from software data. However, what methods and conclusions should we endorse as we move from analytics in-the-small (dealing with a hand…