Jaganmohan Chandrasekaran

DeltaExplainer: A Software Debugging Approach to Generating Counterfactual Explanations

D. R. Kuhn, Yu Lei, R. Kacker, 2022, 2022 IEEE International Conference On Artificial Intelligence Testing (AITest).

A Combinatorial Approach to Fairness Testing of Machine Learning Models

D. R. Kuhn, Yu Lei, R. Kacker, 2022, 2022 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW).

A Combinatorial Approach to Explaining Image Classifiers

D. Richard Kuhn, Raghu Kacker, D. R. Kuhn, 2021, 2021 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW).

A Combinatorial Approach to Testing Deep Neural Network-based Autonomous Driving Systems

D. Richard Kuhn, Raghu Kacker, D. R. Kuhn, 2021, 2021 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW).

DeepFarm: AI-Driven Management of Farm Production using Explainable Causality

Feras A. Batarseh, M. Gopinath, Flora Haberkorn, 2022, 2022 IEEE 29th Annual Software Technology Conference (STC).

Effectiveness of dataset reduction in testing machine learning algorithms

D. Richard Kuhn, Raghu Kacker, D. R. Kuhn, 2020, 2020 IEEE International Conference On Artificial Intelligence Testing (AITest).

Applying Combinatorial Testing to Data Mining Algorithms

Jeff Yu Lei, Raghu Kacker, D. Richard Kuhn, 2017, 2017 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW).

A Survey of Data Security: Practices from Cybersecurity and Challenges of Machine Learning

Erin Lanus, Jaganmohan Chandrasekaran, Padmaksha Roy, 2023, ArXiv.

Test & Evaluation Best Practices for Machine Learning-Enabled Systems

Erin Lanus, Jaganmohan Chandrasekaran, Laura Freeman, 2023, ArXiv.