I highly value the opportunity to mentor and train future leaders in software engineering through dedicated supervision. I foster productive relationships with my students by providing comprehensive research skills training, maintaining open communication, and creating an approachable and supportive environment. I encourage students to think independently, critically engage with their projects, and refine their writing skills. Additionally, I am committed to guiding them in developing effective time-management skills within a research setting. Recently, many of my Ph.D. students have built strong publication records, positioning them for the next stage of their careers.

Faculty

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Dr. Kla Tantithamthavorn

Team Lead

Degree Qualification

  • Postdoctoral Research Fellowship with Professor Ahmed E. Hassan at Queen’s University, Canada. 2017.
  • Doctoral Degree of Engineering (Software Engineering) at Nara Institute of Science and Technology, Japan. 2016.
    • NAIST Best PhD Student Award.
    • Japan Society for the Promotion of Science’s Research Fellowship for Young Scientist 2016-2017 (sole-CI, JPY 8,000,000).
    • NEC C&C Research Award for Non-Japanese Researcher (sole-CI, JPY 200,000)
    • Outstanding Paper Award for Young NEC C&C Researchers by NEC C&C Cooperation, Tokyo, Japan.
    • ACM SIGSOFT CAPS Merit-based Award.
    • Visiting PhD Student Scholarship at Queen’s University, Canada. 2015-2016.
  • Master Degree of Engineering (Software Engineering) at Nara Institute of Science and Technology, Japan. 2014.
    • JASSO Scholarship.
    • Research Assistant Scholarship.
    • Visiting Master Student Scholarship at Queen’s University, Canada. 2014.
  • Bachelor Degree of Engineering (Computer Engineering) at Kasetsart University, Thailand.
    • Research Assistant at Massive Information & Knowledge Engineering Laboratory (Mike Lab)

Professional Training Certificates

  • Graduate Research Supervision Accreditation at Monash University, Australia. 2025.
  • Graduate Certificate in Emerging Academic Leader Program at Monash University, Australia. 2022.
  • Graduate Certificate in Foundations for Effective Teaching at Monash University, Australia. 2019.
  • Regression Modelling Strategies, Vanderbuilt University, 2015.


PhD Students

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Ray Yang

PhD Student (2024-current), Main Superviser.

Thesis Title: Enhancing LLM Guardrails

Google Scholar: N/A

Publications: N/A


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Wannita Takerngsaksiri

PhD Student (2022-2025), Main Superviser.

Thesis Title: Towards the Design and Evaluation of Practical Automated Software Development Tools

Google Scholar: https://scholar.google.com.au/citations?user=Yzpk-0AAAAAJ&hl=en&oi=ao

Publications:

  1. Wannita Takerngsaksiri, Micheal Fu, Chakkrit Tantithamthavorn, Jirat Pasuksmit, Kun Chen, Ming Wu, Code Readability in the Age of Large Language Models: An Industrial Case Study from Atlassian. Under Review.
  2. Wannita Takerngsaksiri, Jirat Pasuksmit, Patanamon Thongtanunam, Chakkrit Tantithamthavorn, Ruixiong Zhang, Fan Jiang, Jing Li, Evan Cook, Kun Chen, Ming Wu: Human-In-the-Loop Software Development Agents. ICSE-SEIP (2025)
  3. Wannita Takerngsaksiri, Rujikorn Charakorn, Chakkrit Tantithamthavorn, Yuan-Fang Li: PyTester: Deep Reinforcement Learning for Text-to-Testcase Generation, Journal of Systems and Software (2025).
  4. Wannita Takerngsaksiri, Chakkrit Tantithamthavorn, Yuan-Fang Li: Syntax-aware on-the-fly code completion. Inf. Softw. Technol. 165: 107336 (2024)
  5. Wannita Takerngsaksiri, Cleshan Warusavitarne, Christian Yaacoub, Matthew Hee Keng Hou, Chakkrit Tantithamthavorn: Students’ Perspectives on AI Code Completion: Benefits and Challenges. COMPSAC 2024: 1606-1611

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Saranya Alagarsamy

PhD Student (2022-2025), Main Superviser.

Thesis Title: N/A

Google Scholar: N/A

Publications:

  1. Saranya Alagarsamy, Chakkrit Tantithamthavorn, Aldeida Aleti, A3Test: Assertion-Augmented Automated Test case generation, Information and Software Technology (2024).




Alumni

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Dr. Michael Fu

Graduated PhD Student (2022-2025), Main Supervisor, Co Superviser by Dr. Trung Le

Thesis Title: Toward More Effective Deep Learning-based Automated Software Vulnerability Prediction, Classification, and Repair

Google Scholar: https://scholar.google.com.au/citations?user=1ndiadMAAAAJ&hl=en

Current: A Lecturer in Software Engineering at the University of Melbourne, Australia.

Publications:

  1. Michael Fu, Chakkrit Tantithamthavorn, Trung Le, Yuki Kume, Van Nguyen, Dinh Q. Phung, John C. Grundy: AIBugHunter: A Practical tool for predicting, classifying and repairing software vulnerabilities. Empir. Softw. Eng. 29(1): 4 (2024)
  2. Michael Fu, Van Nguyen, Chakkrit Tantithamthavorn, Dinh Phung, Trung Le: Vision Transformer Inspired Automated Vulnerability Repair. ACM Trans. Softw. Eng. Methodol. 33(3): 78:1-78:29 (2024)
  3. Jinqiang Yu, Michael Fu, Alexey Ignatiev, Chakkrit Tantithamthavorn, Peter J. Stuckey: A Formal Explainer for Just-In-Time Defect Predictions. ACM Trans. Softw. Eng. Methodol. 33(7): 187:1-187:31 (2024)
  4. Michael Fu, Chakkrit Tantithamthavorn: GPT2SP: A Transformer-Based Agile Story Point Estimation Approach. IEEE Trans. Software Eng. 49(2): 611-625 (2023)
  5. Michael Fu, Van Nguyen, Chakkrit Kla Tantithamthavorn, Trung Le, Dinh Q. Phung: VulExplainer: A Transformer-Based Hierarchical Distillation for Explaining Vulnerability Types. IEEE Trans. Software Eng. 49(10): 4550-4565 (2023)
  6. Michael Fu, Chakkrit Kla Tantithamthavorn, Van Nguyen, Trung Le: ChatGPT for Vulnerability Detection, Classification, and Repair: How Far Are We? APSEC 2023: 632-636
  7. Michael Fu: Toward More Effective Deep Learning-based Automated Software Vulnerability Prediction, Classification, and Repair. ICSE Companion 2023: 208-212
  8. Michael Fu, Chakkrit Tantithamthavorn: LineVul: A Transformer-based Line-Level Vulnerability Prediction. MSR 2022: 608-620
  9. Michael Fu, Chakkrit Tantithamthavorn, Trung Le, Van Nguyen, Dinh Q. Phung: VulRepair: a T5-based automated software vulnerability repair. ESEC/SIGSOFT FSE 2022: 935-947, 2021
  10. Chanathip Pornprasit, Chakkrit Tantithamthavorn, Jirayus Jiarpakdee, Michael Fu, Patanamon Thongtanunam:PyExplainer: Explaining the Predictions of Just-In-Time Defect Models. ASE 2021: 407-418

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Dr. Chanathip Pornprasit

Graduated PhD Student (2021-2025), Main Supervisor

Thesis Title: Context-Aware AI-Powered Code Review Assistant

Google Scholar: https://scholar.google.com/citations?user=DQi7qgwAAAAJ&hl=en

Current: Moving to Industry

Publications:

  1. Chanathip Pornprasit, Chakkrit Tantithamthavorn: Fine-tuning and prompt engineering for large language models-based code review automation. Inf. Softw. Technol. 175: 107523 (2024)
  2. Chanathip Pornprasit, Chakkrit Kla Tantithamthavorn: DeepLineDP: Towards a Deep Learning Approach for Line-Level Defect Prediction. IEEE Trans. Software Eng. 49(1): 84-98 (2023)
  3. Chanathip Pornprasit, Chakkrit Tantithamthavorn, Patanamon Thongtanunam, Chunyang Chen: D-ACT: Towards Diff-Aware Code Transformation for Code Review Under a Time-Wise Evaluation. SANER 2023: 296-307
  4. Patanamon Thongtanunam, Chanathip Pornprasit, Chakkrit Tantithamthavorn: AutoTransform: Automated Code Transformation to Support Modern Code Review Process. ICSE 2022: 237-248, 2021
  5. Chanathip Pornprasit, Chakkrit Tantithamthavorn, Jirayus Jiarpakdee, Michael Fu, Patanamon Thongtanunam: PyExplainer: Explaining the Predictions of Just-In-Time Defect Models. ASE 2021: 407-418
  6. Chanathip Pornprasit, Chakkrit Tantithamthavorn: JITLine: A Simpler, Better, Faster, Finer-grained Just-In-Time Defect Prediction. MSR 2021: 369-379

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Dr. Aastha Pant

Graduated PhD Student (2022-2025), Co Supervisor, Main Superviser by Professor Rashina Hoda

Thesis Title: Understanding Ethics in AI Software Development through Practitioners’ Perspectives and Experiences

Google Scholar: https://scholar.google.com.au/citations?user=BiK56tIAAAAJ&hl=en

Current: Research Assistant at Monash University

Publications:

  1. Aastha Pant, Rashina Hoda, Burak Turhan, Chakkrit Tantithamthavorn: What Do AI/ML Practitioners Think About AI/ML Bias? IEEE Softw. 42(1): 114-118 (2025)
  2. Aastha Pant, Rashina Hoda, Chakkrit Tantithamthavorn, Burak Turhan: Ethics in AI through the practitioner’s view: a grounded theory literature review. Empir. Softw. Eng. 29(3): 67 (2024)
  3. Aastha Pant, Rashina Hoda, Simone V. Spiegler, Chakkrit Tantithamthavorn, Burak Turhan: Ethics in the Age of AI: An Analysis of AI Practitioners’ Awareness and Challenges. ACM Trans. Softw. Eng. Methodol. 33(3): 80:1-80:35 (2024)
  4. Wei Teo, Ze Teoh, Dayang Abang Arabi, Morad Aboushadi, Khairenn Lai, Zhe Ng, Aastha Pant, Rashina Hoda, Chakkrit Tantithamthavorn, Burak Turhan: What Would You do? An Ethical AI Quiz. ICSE Companion 2023: 112-116

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Dr. Yang Hong

Graduated PhD Student (2021-2024), Main Supervisor, Co-supervised with Professor Aldeida Aleti

Thesis Title: Towards Automated Support for Effective Modern Code Review Activities

Google Scholar: https://scholar.google.com.au/citations?user=xe_sbqkAAAAJ&hl=en

Current: Data Engineer at Macquiarie Group.

Publications:

  1. Yang Hong, Chakkrit Tantithamthavorn, Patanamon Thongtanunam, Aldeida Aleti: Don’t forget to change these functions! recommending co-changed functions in modern code review. Inf. Softw. Technol. 176: 107547 (2024)
  2. Yang Hong, Chakkrit Tantithamthavorn, Jirat Pasuksmit, Patanamon Thongtanunam, Arik Friedman, Xing Zhao, Anton Krasikov: Practitioners’ Challenges and Perceptions of CI Build Failure Predictions at Atlassian. SIGSOFT FSE Companion 2024: 370-381
  3. Yang Hong, Chakkrit Tantithamthavorn, Patanamon Thongtanunam, Aldeida Aleti: CommentFinder: a simpler, faster, more accurate code review comments recommendation. ESEC/SIGSOFT FSE 2022: 507-519
  4. Yang Hong, Chakkrit Kla Tantithamthavorn, Patanamon Thongtanunam: Where Should I Look at? Recommending Lines that Reviewers Should Pay Attention To. SANER 2022: 1034-1045

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Dr. Han Hu

*Graduated PhD Student (2022-2024), Co-Main Supervisor with Dr. Chunyang Chen**

Thesis Title: Towards Automated Cross-platform App GUI Development

Google Scholar: https://scholar.google.com/citations?user=8vqgN5gAAAAJ&hl=zh-CN

Current: Research Scientist at Huawei Hong Kong.


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Dr. Yue Liu

Graduated PhD Student (2020-2024), Main Supervisor, Co-supervised with Dr. Li Li.

Thesis Title: Towards Reliable LLM-based Software Development Tools

Google Scholar: https://scholar.google.com.au/citations?user=waVL0PgAAAAJ&hl=en&oi=ao

Current: Research Assistant at Monash University, Australia

Publications:

  1. Yue Liu, Thanh Le-Cong, Ratnadira Widyasari, Chakkrit Tantithamthavorn, Li Li, Xuan-Bach Dinh Le, David Lo: Refining ChatGPT-Generated Code: Characterizing and Mitigating Code Quality Issues. ACM Trans. Softw. Eng. Methodol. 33(5): 116:1-116:26 (2024)
  2. Yue Liu, Chakkrit Tantithamthavorn, Yonghui Liu, Li Li: On the Reliability and Explainability of Language Models for Program Generation. ACM Trans. Softw. Eng. Methodol. 33(5): 126:1-126:26 (2024)
  3. Yue Liu, Chakkrit Tantithamthavorn, Yonghui Liu, Patanamon Thongtanunam, Li Li: Automatically Recommend Code Updates: Are We There Yet? ACM Trans. Softw. Eng. Methodol. 33(8): 217:1-217:27 (2024)
  4. Yue Liu, Chakkrit Tantithamthavorn, Li Li: Protect Your Secrets: Understanding and Measuring Data Exposure in VSCode Extensions. SANER’25.
  5. Yue Liu, Chakkrit Tantithamthavorn, Li Li, Yepang Liu: Deep Learning for Android Malware Defenses: A Systematic Literature Review. ACM Comput. Surv. 55(8): 153:1-153:36 (2023)
  6. Haonan Hu, Yue Liu, Yanjie Zhao, Yonghui Liu, Xiaoyu Sun, Chakkrit Tantithamthavorn, Li Li: Detecting Temporal Inconsistency in Biased Datasets for Android Malware Detection. ASEW 2023: 17-23
  7. Yue Liu, Chakkrit Tantithamthavorn, Li Li, Yepang Liu: Explainable AI for Android Malware Detection: Towards Understanding Why the Models Perform So Well? ISSRE 2022: 169-180

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Dr. Danushka Liyanage

Graduated PhD Student (2019-2023), Co-Main Supervisor with Dr. Marcel Böhme.

Thesis Title: Quantitative Decision-Making for Automated Software Testing

Google Scholar: https://scholar.google.com/citations?user=vSGvrDQAAAAJ&hl=en

Current: Research Fellow at the University of Sydney, Australia

Publications:

  1. Danushka Liyanage, Seongmin Lee, Chakkrit Tantithamthavorn, Marcel Böhme: Extrapolating Coverage Rate in Greybox Fuzzing. ICSE 2024: 132:1-132:12 2023
  2. Danushka Liyanage, Marcel Böhme, Chakkrit Tantithamthavorn, Stephan Lipp: Reachable Coverage: Estimating Saturation in Fuzzing. ICSE 2023: 371-383

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Dr. Jirayus Jiarpakdee

Graduated PhD Student (2018-2021), Main Supervisor, Co-Supervised with Professor John Grundy.

Thesis Title: Towards Explainable Software Defect Prediction Models to Support SQA Planning

Google Scholar: https://scholar.google.co.jp/citations?user=IKCcceAAAAAJ&hl=en

Current: Data Scientist at Siam Commercial Bank (SCB)

Publications:

  1. Anjana Perera, Aldeida Aleti, Chakkrit Tantithamthavorn, Jirayus Jiarpakdee, Burak Turhan, Lisa Kuhn, Katie Walker: Search-based fairness testing for regression-based machine learning systems. Empir. Softw. Eng. 27(3): 79 (2022)
  2. Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, Hoa Khanh Dam, John C. Grundy: An Empirical Study of Model-Agnostic Techniques for Defect Prediction Models. IEEE Trans. Software Eng. 48(2): 166-185 (2022)
  3. Dilini Rajapaksha, Chakkrit Tantithamthavorn, Jirayus Jiarpakdee, Christoph Bergmeir, John Grundy, Wray L. Buntine: SQAPlanner: Generating Data-Informed Software Quality Improvement Plans. IEEE Trans. Software Eng. 48(8): 2814-2835 (2022) 2021
  4. Chakkrit Tantithamthavorn, Jirayus Jiarpakdee, John Grundy: Actionable Analytics: Stop Telling Me What It Is; Please Tell Me What To Do. IEEE Softw. 38(4): 115-120 (2021)
  5. Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, Ahmed E. Hassan: The Impact of Correlated Metrics on the Interpretation of Defect Models. IEEE Trans. Software Eng. 47(2): 320-331 (2021)
  6. Chakkrit Tantithamthavorn, Jirayus Jiarpakdee: Explainable AI for Software Engineering. ASE 2021: 1-2
  7. Chanathip Pornprasit, Chakkrit Tantithamthavorn, Jirayus Jiarpakdee, Michael Fu, Patanamon Thongtanunam: PyExplainer: Explaining the Predictions of Just-In-Time Defect Models. ASE 2021: 407-418
  8. Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, John C. Grundy: Practitioners’ Perceptions of the Goals and Visual Explanations of Defect Prediction Models. MSR 2021: 432-443
  9. Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, Christoph Treude: The impact of automated feature selection techniques on the interpretation of defect models. Empir. Softw. Eng. 25(5): 3590-3638 (2020)
  10. Chaiyakarn Khanan, Worawit Luewichana, Krissakorn Pruktharathikoon, Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, Morakot Choetkiertikul, Chaiyong Ragkhitwetsagul, Thanwadee Sunetnanta: JITBot: An Explainable Just-In-Time Defect Prediction Bot. ASE 2020: 1336-1339
  11. Jirayus Jiarpakdee: Towards a more reliable interpretation of defect models. ICSE (Companion Volume) 2019: 210-213
  12. Suraj Yatish, Jirayus Jiarpakdee, Patanamon Thongtanunam, Chakkrit Tantithamthavorn: Mining software defects: should we consider affected releases? ICSE 2019: 654-665, 2018
  13. Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, Christoph Treude: AutoSpearman: Automatically Mitigating Correlated Software Metrics for Interpreting Defect Models. ICSME 2018: 92-103
  14. Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, Christoph Treude: Artefact: An R Implementation of the AutoSpearman Function. ICSME 2018: 711