Suvodeep Majumder

College of Engineering

Works (8)

Updated: March 18th, 2024 08:21

2024 article

When less is more: on the value of “co-training” for semi-supervised software defect predictors

Majumder, S., Chakraborty, J., & Menzies, T. (2024, February 24). Empirical Software Engineering, Vol. 29.

By: S. Majumder n, J. Chakraborty n & T. Menzies n 

author keywords: Semi-supervised learning; SSL; Self-training; Co-training; Boosting methods; Semi-supervised preprocessing; Clustering-based semi-supervised preprocessing; Intrinsically semi-supervised methods; Graph-based methods; Co-forest; Effort aware tri-training
topics (OpenAlex): Software Engineering Research; Software Reliability and Analysis Research; Software Testing and Debugging Techniques
Sources: Web Of Science, NC State University Libraries
Added: March 11, 2024

2023 article

A deep learning synthetic likelihood approximation of a non-stationary spatial model for extreme streamflow forecasting

Majumder, R., & Reich, B. J. (2023, May 8). Spatial Statistics, Vol. 55.

By: R. Majumder  n & B. Reich n 

Contributors: R. Majumder  n & B. Reich n 

author keywords: Climate change; Deep learning; Density regression; Gaussian process; Max -stable processes; Vecchia approximation
topics (OpenAlex): Hydrology and Drought Analysis; Climate variability and models; Hydrology and Watershed Management Studies
TL;DR: A non-stationary process mixture model (NPMM) for annual streamflow maxima over the central US which uses downscaled climate model precipitation projections to forecast extremal streamflow and is flexible with desirable tail dependence properties, but yields an intractable likelihood. (via Semantic Scholar)
UN Sustainable Development Goals Color Wheel
UN Sustainable Development Goal Categories
13. Climate Action (Web of Science; OpenAlex)
Sources: Web Of Science, NC State University Libraries, ORCID
Added: June 19, 2023

2023 article

Fair Enough: Searching for Sufficient Measures of Fairness

Majumder, S., Chakraborty, J., Bai, G. R., Stolee, K. T., & Menzies, T. (2023, March 16). ACM Transactions on Software Engineering and Methodology, Vol. 32.

By: S. Majumder n, J. Chakraborty n, G. Bai n, K. Stolee n & T. Menzies n 

author keywords: Software fairness; fairness metrics; clustering; theoretical analysis; empirical analysis
topics (OpenAlex): Ethics and Social Impacts of AI; Adversarial Robustness in Machine Learning; Explainable Artificial Intelligence (XAI)
TL;DR: This article shows that many of those fairness metrics effectively measure the same thing, and it is no longer necessary (or even possible) to satisfy all fairness metrics. (via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: October 31, 2023

2022 article

Fair-SSL

Chakraborty, J., Majumder, S., & Tu, H. (2022, May 19).

By: J. Chakraborty n, S. Majumder n & H. Tu n

author keywords: Machine Learning with and for SE; Ethics in Software Engineering
topics (OpenAlex): Ethics and Social Impacts of AI; Intellectual Property and Patents; Digitalization, Law, and Regulation
TL;DR: This is the first SE work where semi-supervised techniques are used to fight against ethical bias in SE ML models, and the clear advantage of Fair-SSL is that it requires only 10% of the labeled training data. (via Semantic Scholar)
Source: Web Of Science
Added: October 3, 2022

2022 article

Methods for stabilizing models across large samples of projects (with case studies on predicting defect and project health)

Majumder, S., Xia, T., Krishna, R., & Menzies, T. (2022, May 23). 2022 MINING SOFTWARE REPOSITORIES CONFERENCE (MSR 2022), pp. 566–578.

By: S. Majumder n, T. Xia n, R. Krishna n & T. Menzies n 

author keywords: Defect Prediction; Project Health; Bellwether; Hierarchical Clustering; Random Forest; Two Phase Transfer Learning; Transfer Learning
topics (OpenAlex): Software Engineering Research; Software Reliability and Analysis Research; Software System Performance and Reliability
TL;DR: This paper provides a promising result showing such stable models can be generated using a new transfer learning framework called STABILIZER, and these case studies are the largest demonstration of the generalizability of quantitative predictions of project quality yet reported in the SE literature. (via Semantic Scholar)
UN Sustainable Development Goals Color Wheel
UN Sustainable Development Goal Categories
9. Industry, Innovation and Infrastructure (OpenAlex)
Sources: Web Of Science, NC State University Libraries
Added: September 19, 2022

2022 article

Revisiting process versus product metrics: a large scale analysis

Majumder, S., Mody, P., & Menzies, T. (2022, March 17). Empirical Software Engineering, Vol. 27.

By: S. Majumder n, P. Mody n & T. Menzies n 

author keywords: Software engineering; Software process; Process metrics; Product metrics; Developer metrics; Random forest; Logistic regression; Support vector machine; HPO
topics (OpenAlex): Software Engineering Research; Software Reliability and Analysis Research; Software System Performance and Reliability
TL;DR: Prior small-scale results are rechecked and it is found that process metrics are better predictors for defects than product metrics, but it is unwise to trust metric importance results from analytics in-the-small studies since those change dramatically when moving to analytics in the-large. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: April 4, 2022

2021 article

Bias in machine learning software: why? how? what to do?

Chakraborty, J., Majumder, S., & Menzies, T. (2021, August 18). 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.

By: J. Chakraborty n, S. Majumder n & T. Menzies n 

author keywords: Software Fairness; Fairness Metrics; Bias Mitigation
topics (OpenAlex): Ethics and Social Impacts of AI; Adversarial Robustness in Machine Learning; Imbalanced Data Classification Techniques
TL;DR: This paper postulates that the root causes of bias are the prior decisions that affect what data was selected and the labels assigned to those examples, and proposes the Fair-SMOTE algorithm, which removes biased labels; and rebalances internal distributions such that based on sensitive attribute, examples are equal in both positive and negative classes. (via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: March 7, 2022

2021 article

Early Life Cycle Software Defect Prediction. Why? How?

Shrikanth, N. C., Majumder, S., & Menzies, T. (2021, May 1). 2021 IEEE/ACM 43RD INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE 2021), pp. 448–459.

By: N. Shrikanth n, S. Majumder n & T. Menzies n 

author keywords: sampling; early; defect prediction; analytics
topics (OpenAlex): Software Engineering Research; Software Reliability and Analysis Research; Software Engineering Techniques and Practices
TL;DR: It is shown that, at least for learning defect predictors, after the first few months, this may not be true, and researchers should adopt a "simplicity-first" approach to their work. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: September 13, 2021

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