About the Author
A/Prof Kla Tantithamthavorn
Associate Professor in Software Engineering
Faculty of Information Technology, Monash University, Australia
Kla Tantithamthavorn is one of the most productive and impactful software engineering researchers of his generation. He holds the position of Associate Professor in the Faculty of Information Technology at Monash University, where he leads research at the intersection of artificial intelligence and software engineering — a field he has helped define.
His work has been cited over 8,600 times (Google Scholar), with an h-index of 44 and 78 publications each cited ten or more times. He has published more than 80 peer-reviewed articles in the most selective venues in his field, including 12 papers in IEEE Transactions on Software Engineering (TSE), 12 papers at the International Conference on Software Engineering (ICSE), and 8 papers in ACM Transactions on Software Engineering and Methodology (TOSEM) — an output that places him among the top researchers worldwide in empirical software engineering.
Research
Kla’s research programme spans three interconnected themes:
AI-Enabled Software Engineering — developing automated techniques for defect prediction, code review automation, and agile planning that help development teams ship higher-quality software faster. His tools are used by practitioners internationally; AIBugHunter, his Visual Studio Code extension for automated vulnerability detection, has been downloaded over 1,000 times.
Explainable AI for Software Engineering (XAI4SE) — a field he helped pioneer, concerned with making AI-driven software quality predictions interpretable and actionable for developers and managers. His open textbook on XAI4SE has attracted over 20,000 pageviews from 4,300 users across 83 countries.
LLM-Based Software Safety and Security (LLMSecOps) — an emerging programme investigating how large language models can be used to find, explain, and fix security vulnerabilities in software systems, and how the vulnerabilities introduced by LLM-generated code can be systematically detected.
Recognition
- World Top 2% Scientist — Stanford University global ranking
- Most Impactful Early Career Researcher in software engineering, 2013–2020
- IEEE Senior Member
- ARC DECRA Fellow (Australian Research Council Discovery Early Career Researcher Award, 2020–2023)
- JSPS Research Fellowship for Young Scientists — Japan Society for the Promotion of Science
- 2024 Dean’s Award for Excellence in Research Engagement and Impact, Monash University
- ACM SIGSOFT Distinguished Paper Award, ASE 2021
- SANER 2025 Most Influential Paper Award
- NAIST Best PhD Student Award
- Finalist, 2024 Defence and National Security Workforce Awards
Funding
Kla has secured over $2 million in competitive research funding, including:
- CSIRO Next Generation Graduate AI Program — $1.2M (2023–2027), supporting PhD scholarships and industry-partnered AI research
- ARC DECRA — $600K (2020–2023), supporting foundational research in explainable AI for software engineering
Mentorship and Teaching
Kla has supervised 13 PhD students (10 as primary supervisor, 3 as co-supervisor), with 6 successfully graduated and placed in academic and industry roles. He brings the same rigour he applies to research to his teaching: he pioneered the use of EdStem’s Unit Testing Challenges for active learning, designed the 2026 Bachelor of Software Engineering curriculum aligned with SWEBOK 2024, and has consistently improved teaching evaluations — from 4.14 in 2023 to 4.57 in 2024.
This book grew from his undergraduate and postgraduate teaching at Monash University, where he has developed and taught courses on software engineering, AI-native development, and automated software quality.
Service
Kla serves the software engineering research community as:
- Associate Editor, IEEE Transactions on Software Engineering
- Guest Editor, IEEE Software (MLOps and Explainable AI for SE special issues)
- Junior PC Co-Chair, Mining Software Repositories (MSR) 2023 and 2025
- Keynote Speaker at ICSE 2023, ASE 2021, and multiple industry partner events
Selected Recent Publications
- Tantithamthavorn et al. (2026). Pitfalls in language models for code intelligence: A taxonomy and survey. ACM TOSEM.
- Tantithamthavorn et al. (2025). Enhancing large language models for text-to-testcase generation. Journal of Systems and Software.
- Tantithamthavorn et al. (2025). RAGVA: Engineering retrieval augmented generation-based virtual assistants in practice. Journal of Systems and Software.
- Tantithamthavorn et al. (2025). Code readability in the age of large language models: An industrial case study from Atlassian. ICSME 2025.
For a complete publication list, see Google Scholar.
Connect: chakkrit.com