Dr Chakkrit Tantithamthavorn
He is a lecturer in the School of Computer Science, the University of Adelaide. He was a postdoctoral research fellow at Queen’s University, Canada. He holds one of the most prestigious and selective sources of national funding in Japan, i.e., a JSPS Research Fellowship for Young Researchers and a Grants-in-Aid for JSPS Fellow. He won the "Best Ph.D. Student Award" for his Ph.D. study at Nara Institute of Science and Technology, Japan. During his Ph.D. study, he also spent two years as a visiting researcher at Queen’s University, Canada. His research has been published at top-tier software engineering venues, such as, IEEE Transactions on Software Engineering (TSE) and the International Conference on Software Engineering (ICSE). His research has been recognized as a TSE Journal-First invited paper at ICSE 2017 and an Outstanding Paper Award for Young C&C Researchers of NEC C&C Foundation, Japan for his ICSE 2015 paper. He was invited to present his research at world-class universities, such as, University College London (UK), McGill University (Canada), École Polytechnique de Montréal (Canada), as well as, academic conferences in the U.S., Canada, UK, Italy, New Zealand, Japan, Thailand, and Argentina. He served a referee of flagship software engineering journals, such as, Applied Soft Computing, IEEE Transactions on Software Engineering, as well as, a program committee member and an additional reviewer of ICSME, MSR, ISEC, SCAM, and SANER. To date, he acquired a total of A$140,000 prestigious research grants for his research projects that are funded by JSPS, NEC C&C Foundation, Queen’s University, MEXT, and NAIST.
His current research lies in software analytics, i.e., the intersection of data science and software engineering, with a specific focus on advancing the fundamentals of predictive and statistical modelling for software engineering (e.g., software analytics) in order to produce more accurate predictions and reliable actionable insights to support software engineers, software managers, data scientists, and researchers. His Ph.D. thesis shows that the experimental components (e.g., the choice of metrics, the quality of dataset) of software analytics modelling substantially impact the predictions and associated insights, suggesting that empirical investigations on the impact of overlooked experimental components are needed to derive practical guidelines.
✓ Eligible to supervise Masters and PhD (as Co-Supervisor) — email supervisor to discuss availability.
Today software development process depends on a variety of development tools (e.g., issue tracking systems, version control systems, code review, continuous integration, continuous deployment, Q&A website). For example, Github—the largest hosting service of source code in the world—currently hosts over 35 millions software repositories, while the last million repositories were generated within 2 months. Millions of software projects also generate large quantities of unstructured software artifacts at a high frequency (so-called Big Data) in many forms, like issue reports, source code, test cases, code reviews, execution logs, app reviews, developer mailing lists, and discussion threads.
Software analytics is a field that focuses on uncovering interesting and actionable knowledge from the unprecedented amount of data in such repositories in order to improve software development, maintenance, evolution, productivity, quality, and user experience. Indeed, many software organizations are eager to be empowered to make data-driven engineering decisions, rather than relying on gut feeling. Also, they use it to identify new opportunities, leading to smarter business moves, more efficient operations, higher profits and happier customers. For example, Microsoft’s data scientists uncover frequently-used commands of Microsoft Windows, which led to an important re-design of user interfaces. Therefore, such insights give the ability to software companies to work faster – and stay agile – give software organizations a competitive edge they didn’t have before.
My mission is to discover the most effective analytical methods and actionable insights for various stakeholders in the software industry. To achieve this, I am working with many wonderful students, collaborators together with industrial partners to make an immediate impact on our society. I use cutting-edge statistical analysis, machine learning, text mining, and data science to develop analytics technologies which specifically turn software engineering data into actionable insight.
|2017||Lecturer||School of Computer Science|
|2017||Award||Best Ph.D. Student Award||Nara Institute of Science and Technology||Japan||—|
|2016||Fellowship||JSPS Research Fellowship for Young Scientists (DC2)||Japan Society for the Promotion of Science (JSPS), Japan.||Japan||4,800,000 JPY|
|English||Can read, write, speak, understand spoken and peer review|
|Thai||Can read, write, speak, understand spoken and peer review|
|2014 - 2016||Nara Institute of Science and Technology||Japan||Ph.D.|
|2012 - 2014||Nara Institute of Science and Technology||Japan||Master|
|2017 - 2017||Postdoctoral Research Fellow||Queen's University||Canada|
|2017||Epigeum: Supervising Doctoral Studies||The University of Adelaide||Australia|
|2015||Regression Modelling Strategies||Vanderbilt University, Nashville||United States|
|2017||Hassan, S., Tantithamthavorn, C., Bezemer, C. -P., & Hassan, A. (2017). Studying the dialogue between users and developers of free apps in the Google Play Store. Empirical Software Engineering, 23(3), 1275-1312.
|2017||Tantithamthavorn, C., McIntosh, S., Hassan, A., & Matsumoto, K. (2017). An Empirical Comparison of Model Validation Techniques for Defect Prediction Models. IEEE Transactions on Software Engineering, 43(1), 1-18.
DOI Scopus15 WoS8
|2016||Tantithamthavorn, C., McIntosh, S., Hassan, A., & Matsumoto, K. (2016). Comments on Researcher Bias: The Use of Machine Learning in Software Defect Prediction. IEEE Transactions on Software Engineering, 42(11), 1092-1094.
DOI Scopus5 WoS3
|—||Jiarpakdee, J., Tantithamthavorn, C., & Hassan, A. E. (n.d.). The Impact of Correlated Metrics on Defect Models.|
|—||Tantithamthavorn, C., Hassan, A. E., & Matsumoto, K. (n.d.). The Impact of Class Rebalancing Techniques on the Performance and
Interpretation of Defect Prediction Models.
|—||Tantithamthavorn, C., McIntosh, S., Hassan, A. E., & Matsumoto, K. (n.d.). The Impact of Automated Parameter Optimization on Defect Prediction
|2018||Tantithamthavorn, C., & Hassan, A. E. (2018). An experience report on defect modelling in practice: pitfalls and challenges.. In F. Paulisch, & J. Bosch (Eds.), ICSE (SEIP) (pp. 286-295). ACM.
|2016||Jiarpakdee, J., Tantithamthavorn, C., Ihara, A., & Matsumoto, K. (2016). A Study of Redundant Metrics in Defect Prediction Datasets. In Proceedings - 2016 IEEE 27th International Symposium on Software Reliability Engineering Workshops, ISSREW 2016 (pp. 51-52). Ottawa, CANADA: IEEE.
|2016||Tantithamthavorn, C., McIntosh, S., Hassan, A., & Matsumoto, K. (2016). Automated parameter optimization of classification techniques for defect prediction models. In L. K. Dillon, W. Visser, & L. Williams (Eds.), Proceedings - International Conference on Software Engineering Vol. 14-22-May-2016 (pp. 321-332). Austin, TX: IEEE.
DOI Scopus32 WoS20
|2016||Tantithamthavorn, C. (2016). Towards a Better Understanding of the Impact of Experimental Components on Defect Prediction Modelling. In L. K. Dillon, W. Visser, & L. Williams (Eds.), 2016 IEEE/ACM 38TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING COMPANION (ICSE-C) (pp. 867-870). Austin, TX: IEEE.
|2015||Thongtanunam, P., Tantithamthavorn, C., Kula, R., Yoshida, N., Iida, H., & Matsumoto, K. (2015). Who should review my code? A file location-based code-reviewer recommendation approach for Modern Code Review. In Y. -G. Guéhéneuc, B. Adams, & A. Serebrenik (Eds.), 2015 IEEE 22nd International Conference on Software Analysis, Evolution, and Reengineering, SANER 2015 - Proceedings (pp. 141-150). Online: IEEE.
DOI Scopus31 WoS19
|2015||Tantithamthavorn, C., McIntosh, S., Hassan, A., Ihara, A., & Matsumoto, K. (2015). The impact of mislabelling on the performance and interpretation of defect prediction models. In A. Bertolino, G. Canfora, & S. Elbaum (Eds.), Proceedings - International Conference on Software Engineering Vol. 1 (pp. 812-823). Online: IEEE.
DOI Scopus25 WoS5
|2014||Tantithamthavorn, C., Ihara, A., Hata, H., & Matsumoto, K. (2014). Impact Analysis of Granularity Levels on Feature Location Technique. In Communications in Computer and Information Science Vol. 432 CCIS (pp. 135-149). Online: Springer.
|2013||Tantithamthavorn, C., Ihara, A., & Matsumoto, K. (2013). Using co-change histories to improve bug localization performance. In S. Takahashi, & R. Leo (Eds.), SNPD 2013 - 14th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (pp. 543-548). Online: IEEE.
DOI Scopus13 WoS1
|2013||Tantithamthavorn, C., Teekavanich, R., Ihara, A., & Matsumoto, K. (2013). Mining A change history to quickly identify bug locations: A case study of the Eclipse project. In 2013 IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2013 (pp. 108-113). Online: IEEE.
DOI Scopus4 WoS2
|2012||Tantithamthavorn, C., & Rungsawang, A. (2012). Knowledge discovery in web traffic log: A case study of facebook usage in Kasetsart University. In JCSSE 2012 - 9th International Joint Conference on Computer Science and Software Engineering (pp. 247-252). Online: IEEE.
- JSPS Research Fellowship for Young Scientists (DC2) from Japan Society for the Promotion of Science (JSPS), Japan. (~$60,000 AUD)
- Grant-in-Aid for JSPS Fellows from Japan Society for the Promotion of Science (JSPS), Japan. (~$16,000 AUD)
- Queen's University Visiting Research Fellowship.
- NAIST Excellent Student Scholarship Award. (~$6,500 AUD)
- NEC C&C Research Grant for Non-Japanese Researchers from NEC C&C Foundation, Japan. (~$18,000 AUD)
- Monbukagakusho Honors Scholarship for Privately-Financed International Students. (~$10,000 AUD)
|Date||Role||Research Topic||Program||Degree Type||Student Load||Student Name|
|2017||Co-Supervisor||Studying the Impact of Experimental Issues on the Interpretation of Defect Prediction Models||Doctor of Philosophy||Doctorate||Full Time||Mr Jirayus Jiarpakdee|