Software Engineering Lab · Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University
Understanding code, and the machines that write it.
CURSOR — Code Understanding, Reliability, and Software Orchestration Research
We do empirical software engineering on how code is understood, how AI systems that generate and review code can be made reliable, and how AI-enabled software is built and orchestrated in practice.
Research
Our work runs from reading code, to trusting the models that write it, to engineering the systems they live in. Each thread stands on its own, and they inform one another.
Understanding
How code is comprehended and reused — code similarity and clone search (Siamese), and frameworks for measuring coding proficiency in Python and JavaScript (pycefr, jscefr).
Reliability
Making AI for code trustworthy — metamorphic testing of language models, efficiency under quantization, and rigorous evaluation of where these tools can be relied on.
Orchestration
Engineering AI-enabled systems — how models, agents, and pipelines are composed into software, and how teams and companies build and maintain it as these parts evolve.
Open thesis topics
Proposed thesis topics for the second semester of academic year 2569 (2026), Department of Computer Engineering.
News
- Oct 2026CURSOR is established at the Faculty of Engineering, Chulalongkorn University.
- 2026Now recruiting graduate and undergraduate students across all three research threads.
Join
We welcome curious students who want to do careful, empirical software engineering research — whether that means measuring how code is understood, stress-testing models that write code, or engineering the systems that put those models to work. Prior research experience is welcome but not required; genuine curiosity is.
If you are a prospective master's or undergraduate student at Chulalongkorn, or a potential collaborator, send a short note about what you would like to work on, along with your CV.
Get in touch