Gregory Gay
Gregory Gay is an Associate Professor in the Software Engineering division at Chalmers and the University of Gothenburg. His research interests include automated testing and analysis and search-based software engineering. Our society depends on complex software. Dr. Gay's research blends optimization, artificial intelligence, and machine learning techniques in order to help developers deliver those systems in a safe, secure, and efficient manner.
Dr. Gay received his Ph.D. from the University of Minnesota, working with the Critical Systems research group, and an M.S. from West Virginia University. He was previously an Assistant Professor at the University of South Carolina.
Personal Webpage: http://greggay.com
Google Scholar: https://scholar.google.com/citations?user=3nwDANgAAAAJ&hl=en
Visar 25 publikationer
Testing, Debugging, and Log Analysis With Modern AI Tools
Improving the Readability of Generated Tests Using GPT-4 and ChatGPT Code Interpreter
Exploring Genetic Improvement of the Carbon Footprint of Web Pages
Developer Views on Software Carbon Footprint and Its Potential for Automated Reduction
Understanding Problem Solving in Software Testing: An Exploration of Tester Routines and Behavior
Automated Support for Unit Test Generation
Test Maintenance for Machine Learning Systems: A Case Study in the Automotive Industry
How Do Different Types of Testing Goals Affect Test Case Design?
Understanding Problem Solving in Software Testing: An Exploration of Tester Routines and Behavior
Search-Based Test Generation Targeting Non-Functional Quality Attributes of Android Apps
Requirements Engineering for Automotive Perception Systems: An Interview Study
The integration of machine learning into automated test generation: A systematic mapping study
Identifying Redundancies and Gaps Across Testing Levels During Verification of Automotive Software
Mutation Testing in Continuous Integration: An Exploratory Industrial Case Study
How Closely are Common Mutation Operators Coupled to Real Faults?
Learning how to search: generating effective test cases through adaptive fitness function selection
Non-Functional Requirements for Machine Learning: An Exploration of System Scope and Interest
Using Machine Learning to Generate Test Oracles: A Systematic Literature Review
Generating Diverse Test Suites for Gson Through Adaptive Fitness Function Selection
Bytecode-Based Multiple Condition Coverage: An Initial Investigation
Understanding The Impact of Solver Choice in Model-Based Test Generation
Defects4J as a Challenge Case for the Search-Based Software Engineering Community
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Visar 1 forskningsprojekt
Context-Infused Automated Software Test Generation