Chakkrit Tantithamthavorn

Chakkrit Tantithamthavorn
School of Computer Science
Faculty of Engineering, Computer and Mathematical Sciences

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.

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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.

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.

 

Appointments

Date Position Institution name
2017 Lecturer School of Computer Science

Awards and Achievements

Date Type Title Institution Name Amount
2017 Best Ph.D. Student Award Nara Institute of Science and Technology
2016 - 2018 JSPS Research Fellowship for Young Scientists (DC2) Japan Society for the Promotion of Science (JSPS), Japan.

Language Competencies

Language Competency
English Can read, write, speak, understand spoken and peer review
Thai Can read, write, speak, understand spoken and peer review

Education

Date Institution name Country Title
2014 - 2016 Nara Institute of Science and Technology Japan Ph.D.
2012 - 2014 Nara Institute of Science and Technology Japan Master

Postgraduate Training

Date Title Institution Country
2017 - 2017 Postdoctoral Research Fellow Queen's University Canada

Certifications

Date Title Institution name Country
2015 Regression Modelling Strategies Vanderbilt University, Nashville United States

Research Interests

Applied Statistics, Computer Software, Knowledge Representation and Machine Learning, Pattern Recognition and Data Mining, Software Engineering

Journals

Date Citation
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 10.1109/TSE.2016.2584050
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 10.1109/TSE.2016.2553030

Conference Papers

Date Citation
2016 Tantithamthavorn,C, 2016, Towards a better understanding of the impact of experimental components on defect prediction modelling. 10.1145/2889160.2889256
2016 Tantithamthavorn,C, McIntosh,S, Hassan,A, Matsumoto,K, 2016, Automated parameter optimization of classification techniques for defect prediction models 10.1145/2884781.2884857
2016 Jiarpakdee,J, Tantithamthavorn,C, Ihara,A, Matsumoto,K, 2016, A Study of Redundant Metrics in Defect Prediction Datasets, 27th IEEE International Symposium on Software Reliability Engineering (ISSRE), Ottawa, CANADA 10.1109/ISSREW.2016.30
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, 2015 IEEE ACM 37th IEEE International Conference on Software Engineering, Florence, ITALY 10.1109/ICSE.2015.93
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, International Conference on Software Analysis, Evolution, and Reengineering (SANER), Montreal, CANADA 10.1109/SANER.2015.7081824
2014 Tantithamthavorn,C, Ihara,A, Hata,H, Matsumoto,K, 2014, Impact Analysis of Granularity Levels on Feature Location Technique, 1st Asia Pacific Requirements Engineering Symposium, APRES 2014, Auckland, New Zealand 10.1007/978-3-662-43610-3_11
2013 Tantithamthavorn,C, Ihara,A, Matsumoto,K, 2013, Using co-change histories to improve bug localization performance, 14th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), Honolulu, HI 10.1109/SNPD.2013.92
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, IEEE 24th International Symposium on Software Reliability Engineering Workshops (ISSREW), Pasadena, CA 10.1109/ISSREW.2013.6688888
2012 Tantithamthavorn,C, Rungsawang,A, 2012, Knowledge discovery in web traffic log: A case study of facebook usage in Kasetsart University, 2012 Joint Conference on Computer Science and Software Engineering (JCSSE), Bangkok, Thailand 10.1109/JCSSE.2012.6261960
  1. JSPS Research Fellowship for Young Scientists (DC2) from Japan Society for the Promotion of Science (JSPS), Japan. (~$60,000 AUD)
  2. Grant-in-Aid for JSPS Fellows from Japan Society for the Promotion of Science (JSPS), Japan. (~$16,000 AUD)
  3. Queen's University Visiting Research Fellowship. 
  4. NAIST Excellent Student Scholarship Award. (~$6,500 AUD)
  5. NEC C&C Research Grant for Non-Japanese Researchers from NEC C&C Foundation, Japan. (~$18,000 AUD)
  6. Monbukagakusho Honors Scholarship for Privately-Financed International Students. (~$10,000 AUD)

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