Biography
Ricardo Reyna received his Bachelor's degree in Computer Information Systems from Texas State University in 2014 and earned his Masters in Software Engineering from the Pennsylvania State University in 2017. He pursued further graduate studies at the Johns Hopkins Whiting School of Engineering in 2018. Ricardo completed his Ph.D. in Systems Engineering at Colorado State University in May 2025 under the supervision of Professor Steve Simske. He currently works as a Senior QA Engineer at Dun & Bradstreet. His research focuses on software testing, computer vision, artificial intelligence (AI), and machine learning (ML).
Research Interests
Computer Vision–Based Software Testing
Automated UI component detection such as YOLO and Detectron2
CV-driven test case generation for web and mobile applications
Detection of UI defects such as misalignment, responsiveness issues, and visual inconsistencies
Test Case Diversity and Effectiveness
Improving functional test coverage and diversity
Comparing manual, automation-based, and CV-driven test design approaches
Quantitative evaluation using precision, recall, F1-score, entropy, and statistical analysis (ANOVA)
AI-Augmented Test Design Using LLMs
Leveraging large language models (e.g., ChatGPT) to enrich and clarify test cases
Automating test documentation and improving QA onboarding processes
Combining CV outputs with NLP for end-to-end intelligent test generation
Exploratory and Heuristic-Based Testing
Using exploratory testing strategies to enhance negative test scenarios
Bridging human testing heuristics with AI-driven automation
Systems Engineering Applications of AI
Applying AI techniques within systems engineering frameworks
Extending CV-based testing approaches to emerging domains (e.g., VR/AR systems)
Automated UI component detection such as YOLO and Detectron2
CV-driven test case generation for web and mobile applications
Detection of UI defects such as misalignment, responsiveness issues, and visual inconsistencies
Test Case Diversity and Effectiveness
Improving functional test coverage and diversity
Comparing manual, automation-based, and CV-driven test design approaches
Quantitative evaluation using precision, recall, F1-score, entropy, and statistical analysis (ANOVA)
AI-Augmented Test Design Using LLMs
Leveraging large language models (e.g., ChatGPT) to enrich and clarify test cases
Automating test documentation and improving QA onboarding processes
Combining CV outputs with NLP for end-to-end intelligent test generation
Exploratory and Heuristic-Based Testing
Using exploratory testing strategies to enhance negative test scenarios
Bridging human testing heuristics with AI-driven automation
Systems Engineering Applications of AI
Applying AI techniques within systems engineering frameworks
Extending CV-based testing approaches to emerging domains (e.g., VR/AR systems)
Teaching Interests
ISAN1323, ISAN 3305, ISAN 3374, ISAN 3382