BEN: A combinatorial testing-based fault localization tool. - Laleh Shikh Gholamhossein Ghandehari; Jaganmohan Chandrasekaran; Yu Lei 001; Raghu Kacker; D. Richard Kuhn (ICST Workshops, 2015)

Evaluating the Effectiveness of BEN in Localizing Different Types of Software Fault. - Jaganmohan Chandrasekaran; Laleh S. Ghandehari; Yu Lei 001; Raghu Kacker; D. Richard Kuhn (ICST Workshops, 2016)

Applying Combinatorial Testing to Data Mining Algorithms. - Jaganmohan Chandrasekaran; Huadong Feng; Yu Lei 001; D. Richard Kuhn; Raghu Kacker (ICST Workshops, 2017)

A Method-Level Test Generation Framework for Debugging Big Data Applications. - Huadong Feng; Jaganmohan Chandrasekaran; Yu Lei 001; Raghu Kacker; D. Richard Kuhn (IEEE BigData, 2018)

Effectiveness of dataset reduction in testing machine learning algorithms. - Jaganmohan Chandrasekaran; Huadong Feng; Yu Lei 001; Raghu Kacker; D. Richard Kuhn (AITest, 2020)

Evaluation of T-Way Testing of DNNs in Autonomous Driving Systems. - Jaganmohan Chandrasekaran; Ankita Ramjibhai Patel; Yu Lei 001; Raghu Kacker; D. Richard Kuhn (AITest, 2021)

A Combinatorial Approach to Explaining Image Classifiers. - Jaganmohan Chandrasekaran; Yu Lei 001; Raghu Kacker; D. Richard Kuhn (ICST Workshops, 2021)

A Combinatorial Approach to Testing Deep Neural Network-based Autonomous Driving Systems. - Jaganmohan Chandrasekaran; Yu Lei 001; Raghu Kacker; D. Richard Kuhn (ICST Workshops, 2021)

DeltaExplainer: A Software Debugging Approach to Generating Counterfactual Explanations. - Sunny Shree; Jaganmohan Chandrasekaran; Yu Lei 001; Raghu N. Kacker; D. Richard Kuhn (AITest, 2022)

Enabling AI Adoption through Assurance. - Jaganmohan Chandrasekaran; Feras A. Batarseh; Laura J. Freeman; Raghu Kacker; M. S. Raunak 001; Rick Kuhn (FLAIRS, 2022)

A Combinatorial Approach to Fairness Testing of Machine Learning Models. - Ankita Ramjibhai Patel; Jaganmohan Chandrasekaran; Yu Lei 001; Raghu N. Kacker; D. Richard Kuhn (ICST Workshops, 2022)

Synthetic Data Generation Using Combinatorial Testing and Variational Autoencoder. - Krishna Khadka; Jaganmohan Chandrasekaran; Yu Lei 001; Raghu N. Kacker; D. Richard Kuhn (ICSTW, 2023)

A Survey of Data Security: Practices from Cybersecurity and Challenges of Machine Learning. - Padmaksha Roy; Jaganmohan Chandrasekaran; Erin Lanus; Laura J. Freeman; Jeremy Werner (CoRR, 2023)

Test & Evaluation Best Practices for Machine Learning-Enabled Systems. - Jaganmohan Chandrasekaran; Tyler Cody; Nicola McCarthy; Erin Lanus; Laura J. Freeman (CoRR, 2023)

A Combinatorial Approach to Hyperparameter Optimization. - Krishna Khadka; Jaganmohan Chandrasekaran; Yu Lei 001; Raghu N. Kacker; D. Richard Kuhn (CAIN, 2024)

Assured Autonomy Through Combinatorial Methods. - D. Richard Kuhn; M. S. Raunak 001; Raghu N. Kacker; Jaganmohan Chandrasekaran; Erin Lanus; Tyler Cody; Laura J. Freeman (Computer, 2024)

Can an LLM Find Its Way Around a Spreadsheet? - Cho-Ting Lee; Andrew Neeser; Shengzhe Xu; Jay Katyan; Patrick Cross; Sharanya Pathakota; Marigold Norman; John Simeone; Jaganmohan Chandrasekaran; Naren Ramakrishnan (ICSE, 2025)

Evaluating Large Language Model Robustness using Combinatorial Testing. - Jaganmohan Chandrasekaran; Ankita Ramjibhai Patel; Erin Lanus; Laura J. Freeman (ICSTW, 2025)

Data Frequency Coverage Impact on AI Performance. - Erin Lanus; Brian Lee; Jaganmohan Chandrasekaran; Laura J. Freeman; M. S. Raunak 001; Raghu N. Kacker; D. Richard Kuhn (ICSTW, 2025)