184 citations · 399 across the 12 of their papers we have counts for
12 papers
DetoxBench: Benchmarking Large Language Models for Multitask Fraud & Abuse Detection
Joymallya Chakraborty, Wei Xia, Anirban Majumder +3
Large language models (LLMs) have demonstrated remarkable capabilities in natural language processing tasks. However, their practical application in high-stake domains, such as fra…
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,…
FairMask: Better Fairness via Model-based Rebalancing of Protected Attributes
Kewen Peng, Joymallya Chakraborty, Tim Menzies
Context: Machine learning software can generate models that inappropriately discriminate against specific protected social groups (e.g., groups based on gender, ethnicity, etc). Mo…
FairBalance: How to Achieve Equalized Odds With Data Pre-processing
Zhe Yu, Joymallya Chakraborty, Tim Menzies
This research seeks to benefit the software engineering society by providing a simple yet effective pre-processing approach to achieve equalized odds fairness in machine learning s…
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…
Making Fair ML Software using Trustworthy Explanation
Joymallya Chakraborty, Kewen Peng, Tim Menzies
Machine learning software is being used in many applications (finance, hiring, admissions, criminal justice) having a huge social impact. But sometimes the behavior of this softwar…
Fairway: A Way to Build Fair ML Software
Joymallya Chakraborty, Suvodeep Majumder, Zhe Yu +1
Machine learning software is increasingly being used to make decisions that affect people's lives. But sometimes, the core part of this software (the learned model), behaves in a b…
Predicting Breakdowns in Cloud Services (with SPIKE)
Jianfeng Chen, Joymallya Chakraborty, Philip Clark +3
Maintaining web-services is a mission-critical task where any down-time means loss of revenue and reputation (of being a reliable service provider). In the current competitive web…
Software Engineering for Fairness: A Case Study with Hyperparameter Optimization
Joymallya Chakraborty, Tianpei Xia, Fahmid M. Fahid +1
We assert that it is the ethical duty of software engineers to strive to reduce software discrimination. This paper discusses how that might be done. This is an important topic sin…
Communication and Code Dependency Effects on Software Code Quality: An Empirical Analysis of Herbsleb Hypothesis
Suvodeep Majumder, Joymallya Chakraborty, Amritanshu Agrawal +1
Prior literature has suggested that in many projects 80\% or more of the contributions are made by a small called group of around 20% of the development team. Most prior studies de…