Publications

Manuscripts Under Review / Preparation

• A Combinatorial Approach to Synthetic Data Generation
  Krishna Khadka, Jaganmohan Chandrasekaran, Yu Lei, Raghu Kacker and D. Richard Kuhn.
  Submitted to a Journal, currently under review

• A Survey of Data Security: Practices from Cybersecurity and Challenges of Machine Learning
  Padmaksha Roy, Jaganmohan Chandrasekaran, Erin Lanus, Laura Freeman, and Jeremy Warner.
  Submitted to a Journal, currently under revision – Preprint

2025

• Data Frequency Coverage Impact on AI Performance
  Erin Lanus, Brian Lee, Jaganmohan Chandrasekaran , Laura Freeman, M S Raunak, Raghu Kacker and Rick Kuhn. Accepted to IWCT 2025 - Preprint

• Evaluating Large Language Model Robustness Using Combinatorial Testing
  Jaganmohan Chandrasekaran, Ankita Ramjibhai Patel, Erin Lanus, and Laura Freeman. Accepted to IWCT 2025 - Preprint

• Can an LLM find its way around a Spreadsheet?
  Cho-Ting Lee, Andrew Nesser, Shengzhe Xu, Jay Katyan, Patrick Cross, Sharanya Pathakota, Marigold Norman, John Simeone, Jaganmohan Chandrasekaran, and Naren Ramakrishnan. Accepted to ICSE 2025

2024

• Key Steps to Fielding Combat Credible AI-Enabled Systems
  Nicola McCarthy, Tyler Cody, Jaganmohan Chandrasekaran, Erin Lanus, and Laura Freeman. In 2024 Naval Engineers Journal – Article

• Testing Machine Learning: Best Practices for the Lifecycle
  Jaganmohan Chandrasekaran, Tyler Cody, Nicola McCarthy, Erin Lanus, and Laura Freeman. In 2024 Naval Engineers Journal – Article – ITEA 2024 Publications Award

• Leveraging Combinatorial Coverage in the Machine Learning Product Lifecycle
  Jaganmohan Chandrasekaran, Erin Lanus, Tyler Cody, Laura Freeman, Raghu Kacker, M S Raunak, and D. Richard Kuhn.
  In 2024 IEEE Computer. – Preprint

• Assured Autonomy through Combinatorial Methods
  D.Richard Kuhn, M S Raunak, Raghu N.Kacker, Jaganmohan Chandrasekaran, Erin Lanus, Tyler Cody, and Laura Freeman
  In 2024 IEEE Computer – Preprint

• A Combinatorial Approach to Hyperparameter Optimization
  Krishna Khadka, Jaganmohan Chandrasekaran, Yu Lei, Raghu Kacker and D. Richard Kuhn.
  In 2024 IEEE International Conference on AI Engineering (CAIN), IEEE/ACM – Article. Distinguished paper Award Candidate

2023

• Synthetic Data Generation Using Combinatorial Testing and Variational Autoencoder
  Krishna Khadka, Jaganmohan Chandrasekaran, Yu Lei, Raghu Kacker and D. Richard Kuhn.
  In 2023 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW) – Preprint.

2022

• DeepFarm: AI-Driven Management of Farm Production using Explainable Causality
  Chelsea Wang, Jaganmohan Chandrasekaran, Flora Haberkorn, Yan Don, Munisamy Gopinath, and Feras A. Batarseh.
  In 2022 IEEE 29th Annual Software Technology Conference (STC), IEEE.
• DeltaExplainer: A Software Debugging Approach to Generating Counterfactual Explanations
  Sunny Shree, Jaganmohan Chandrasekaran , Yu Lei, D. Richard Kuhn, and Raghu Kacker – Article.
  In 2022 IEEE International Conference On Artificial Intelligence Testing (AITest), IEEE.
• Enabling AI Adoption through Assurance, (Tutorial),
  Jaganmohan Chandrasekaran, Feras A. Batarseh, Laura Freeman, D. Richard Kuhn, M S Raunak, and Raghu N. Kacker
  In 2022 Florida Artificial Intelligence Research Society. – Preprint
• A Combinatorial Approach to Fairness Testing of Machine Learning Models
  Ankita Ramjibhai Patel, Jaganmohan Chandrasekaran, Yu Lei, Raghu Kacker, and D. Richard Kuhn.
  In 2022 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). – Preprint

2021

• Evaluation of T-Way Testing of DNNs in Autonomous Driving Systems
  Jaganmohan Chandrasekaran ,Ankita Ramjibhai Patel, Yu Lei, Raghu Kacker, and D. Richard Kuhn.
  In 2021 IEEE International Conference On Artificial Intelligence Testing (AITest). IEEE.– Preprint
• A Combinatorial Approach to Explaining Image Classifiers
  Jaganmohan Chandrasekaran ,Yu Lei, Raghu Kacker, and D. Richard Kuhn.
  In 2021 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). – Preprint
• A Combinatorial Approach to Testing Deep Neural Network-based Autonomous Driving Systems
  Jaganmohan Chandrasekaran ,Yu Lei, Raghu Kacker, and D. Richard Kuhn.
  In 2021 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). – Preprint

2020

• Effectiveness of dataset reduction in testing machine learning algorithms.
  Jaganmohan Chandrasekaran , Huadong Feng, Yu Lei, Raghu Kacker, and D. Richard Kuhn.
  In 2020 IEEE International Conference On Artificial Intelligence Testing (AITest). – Preprint

2018

• A Method-Level Test Generation Framework for Debugging Big Data Applications.
  Huadong Feng, Jaganmohan Chandrasekaran , Yu Lei, D. Richard Kuhn, and Raghu Kacker.
  In 2018 IEEE International Conference on Big Data (Big Data). – Preprint

2017

• Applying Combinatorial Testing to Data Mining Algorithms.
  Jaganmohan Chandrasekaran , Huadong Feng, Yu Lei, D. Richard Kuhn, and Raghu Kacker.
  In 2017 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). – Preprint

2016

• Evaluating the Effectiveness of BEN in Localizing Different Types of Software Fault
  Jaganmohan Chandrasekaran , Laleh Sh Ghandehari, Yu Lei, Raghu Kacker, and D. Richard Kuhn.
  In 2016 International Conference on Software Testing, Verification and Validation Workshops (ICSTW) – Article

2015

• BEN: A combinatorial testing-based fault localization tool.
  Laleh Sh Ghandehari, Jaganmohan Chandrasekaran, Yu Lei, Raghu Kacker, and D. Richard Kuhn.
  In 2015 International Conference on Software Testing, Verification and Validation Workshops (ICSTW) – Article