Kevin A. Schneider
Professor
University of Arkansas for Medical Sciences
faculty
Research Areas
Links
Is this your profile? Verify and claim your profile
Biography and Research Information
OverviewAI-generated summary
Kevin A. Schneider's research focuses on the application of advanced computational techniques, particularly deep learning and large language models, to address complex problems in software engineering and data analysis. He has investigated methods for improving the interpretability of machine learning models, such as the development of the C-SHAP hybrid method. His work also explores the generation and analysis of code, including the creation of benchmarks for evaluating semantic clones and the automated generation of user stories from natural language using large language models.
Schneider has studied the reproducibility of programming-related issues found in online forums like Stack Overflow and has explored the automated derivation of technical diagrams, such as UML sequence diagrams, from user requirements. His research interests extend to medical applications, including the development of deep transfer learning models for histopathology segmentation in colorectal cancer detection. With a significant publication record (218 total publications) and a high citation count (3,939 total citations), Schneider is recognized as a highly cited researcher, indicated by his h-index of 33.
Metrics
- h-index: 33
- Publications: 218
- Citations: 3,939
Selected Publications
- Quantum software engineering and potential of quantum computing in software engineering research: a review (2025) DOI
- Comparative analysis of quantum and classical support vector classifiers for software bug prediction: an exploratory study (2025) DOI
- Quantum Software Engineering and Potential of Quantum Computing in Software Engineering Research: A Review (2025) DOI
- C-SHAP: A Hybrid Method for Fast and Efficient Interpretability (2025) DOI
- Evaluating the Performance of a D-Wave Quantum Annealing System for Feature Subset Selection in Software Defect Prediction (2024) DOI
- Take Loads Off Your Developers: Automated User Story Generation using Large Language Model (2024) DOI
- Automated Derivation of UML Sequence Diagrams from User Stories: Unleashing the Power of Generative AI vs. a Rule-Based Approach (2024) DOI
- Reproducibility Challenges of External Computational Experiments in Scientific Workflow Management Systems (2024) DOI
- A Study of Backporting Code in Open-Source Software for Characterizing Changesets (2024) DOI
- ReBack: recommending backports in social coding environments (2024) DOI
- Active Clones: Source Code Clones at Runtime (2024) DOI
- Late Propagation in Near-Miss Clones: An Empirical Study (2024) DOI
- GPTCloneBench: A comprehensive benchmark of semantic clones and cross-language clones using GPT-3 model and SemanticCloneBench (2023) DOI
- Unveiling the Potential of Large Language Models in Generating Semantic and Cross-Language Clones (2023) DOI
- GPTCloneBench: A comprehensive benchmark of semantic clones and cross-language clones using GPT-3 model and SemanticCloneBench (2023) DOI
Similar Researchers
Based on overlapping research topics