CRISPE
CRISPE is a semantic-guided execution planning approach for code coverage prediction. It is one of my recent research projects and was published at FSE 2025.
Learn MoreAbout Me
I am a Ph.D. candidate in Computer Science at The University of Texas at Dallas, advised by Dr. Tien N. Nguyen. My research focuses on AI-assisted software engineering, testing, fuzzing, web automation, and Large Language Models for improving software reliability.
I am currently working as an AI Researcher with DMI (Digital Management, LLC), where I help build NLP and LLM-based systems for automated compliance evaluation and AI-assisted code migration.
- Research lab: AI4Code Lab
- Institution: The University of Texas at Dallas
- GitHub: github.com/dhulipalahridya
- Email: hridya.dhulipala@utdallas.edu
Research Interests
- AI-assisted Web Testing
- LLM-based Fuzzing
- Software Security
- Program Analysis
- Web Automation
Projects
Planning to Guide LLM for Code Coverage Prediction
This project uses planning with large language models to improve code coverage prediction by guiding execution based on program semantics.
Learn MoreAI-Assisted Web Testing
A research direction focused on using LLMs and automation to generate and verify web test scripts more effectively.
Learn MoreEducation
- Ph.D. in Computer Science, The University of Texas at Dallas
- M.S. in Computer Science, The University of Texas at Dallas
- B.E. in Electronics & Communication, BMS College of Engineering
Experience
- AI Researcher, DMI (with UTD)
- Business Technology Analyst, Deloitte
- Software & Intelligence Intern, Ather Energy
Skills
- Python
- Java
- JavaScript
- SQL
- Selenium
- Playwright
- JUnit
- Angular
- Node.js
Contact
Email: hridya.dhulipala@utdallas.edu
GitHub: github.com/dhulipalahridya
LinkedIn: linkedin.com/in/yourprofile