I’m Kla (published papers as Chakkrit Tantithamthavorn) (IEEE Senior Member, ex-ARC DECRA Fellow, ex-JSPS Fellow), an Associate Professor in Software Engineering and the Course Director for Bachelor of Software Engineering (Honours) in the Faculty of Information Technology, Monash University, Australia. Research. My research is focused on developing AI-enabled software development techniques (e.g., AI for Software Defects, AI for Code Review, and AI for Agile) and tools (e.g, AIBugHunter) in order to help developers find defects faster, improve developers’ productivity, make better data-informed decisions, and better improve the quality of software systems. In addition, I’m currently investigating new tools and techniques that enable software engineers to test, safeguard, and monitor the safety and security risks of LLM-based software systems at scale (called LLMSecOps).

With the rise of software systems ranging from personal assistance to the nation's facilities, software defects become more critical concerns as they can cost millions of dollar as well as impact human lives. Yet, at the breakneck pace of rapid software development settings (like DevOps paradigm), the Quality Assurance (QA) practices nowadays are still time-consuming. Continuous Analytics for Software Quality (i.e., defect prediction models) can help development teams prioritize their QA resources and chart better quality improvement plan to avoid pitfalls in the past that lead to future software defects. Due to the need of specialists to design and configure a large number of configurations (e.g., data quality, data preprocessing, classification techniques, interpretation techniques), a set of practical guidelines for developing accurate and interpretable defect models has not been well-developed. 

The ultimate goal of my research aims to (1) provide practical guidelines on how to develop accurate and interpretable defect models for non-specialists; (2) develop an intelligible defect model that offer suggestions how to improve both software quality and processes; and (3) integrate defect models into a real-world practice of rapid development cycles like CI/CD settings. My research project is expected to provide significant benefits including the reduction of software defects and operating costs, while accelerating development productivity for building software systems in many of Australia's critical domains such as Smart Cities and e-Health.

I'm available to supervise Honours/Master/PhD students. Please feel free to contact me if you are interested.