Projects per year
Personal profile
Biography
Dr Tantithamthavorn is a Monash's Software Engineering Discipline group lead, a 2020 ARC DECRA Fellow, and a Software Engineering researcher in the Faculty of Information Technology, Monash University, Australia.
Dr Tantithamthavorn is interested in the broad area of software engineering, explainable AI, digital health, and social goods. He is best known for his high impact research in the area of defect prediction models, a lead instructor of the MSR'19 Tutorial on Software Analytics, and the core developer of the ScottKnott ESD test R package (14,000+ downloads).
Dr Tantithamthavorn's main research is focusing on developing practical and explainable analytics to prevent future software defects. His research has been published at flagship software engineering venues, such as IEEE Transactions on Software Engineering (TSE), Empirical Software Engineering (EMSE), and the International Conference on Software Engineering (ICSE).
Aligned with the Faculty's mission, 'IT for Social Good', Dr Tantithamthavorn is interested in the area of Digital Health. For example, he plays a significant role in developing analytics systems for estimating patient's wait-time in Emergency Departments. Currently, this analytics system has been deployed in many hospitals in Australia (e.g., St Vincent Hospital, Cabrini, and Eastern Health).
Currently, Dr Tantithamthavorn serves on the Review Board of the IEEE Transactions on Software Engineering (TSE), the Open Science Review board of Empirical Software Engineering Journal (EMSE), and is/was a program committee member of ICSE, FSE, ASE, MSR, ICSME, SANER, and is/was a referee of several software engineering journals of TSE, EMSE, AuSE, IST, JSS, JSEP, IST.
Research interests
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.
Supervision interests
I'm available to supervise Honours/Master/PhD students. Please feel free to contact me if you are interested.
Education/Academic qualification
Software Engineering, Doctor of Engineering, Nara Institute of Science and Technology
Award Date: 26 Sep 2016
Software Engineering, Master of Engineering, Nara Institute of Science and Technology
Award Date: 31 Mar 2014
Research area keywords
- Software Engineering
- Empirical Software Engineering
- Software Quality Assurance
- Machine Learning
- Statistical and Data Analysis
- Deep Learning for Cyber Security
- Artificial intelligence
Network
Projects
- 2 Active
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Practical and Explainable Analytics to Prevent Future Software Defects
Australian Research Council (ARC)
2/03/20 → 1/03/23
Project: Research
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Investigation of the utility and the user experiences of widespread access to accurate emergency department wait times.
Walker, K., Ben-Mier, M., Joe, K., Papatheohari, J., Rankin, D., Stephenson, M., Martini, E., Lowthian, J., Stephenson, M., Blecher, G., Rodda, H., Turhan, B., Tantithamthavorn, C., Aleti, A. & Jiarpakdee, J.
Department of Health (Australia)
1/07/19 → 30/06/21
Project: Research
Research output
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An empirical study of model-agnostics techniques for defect prediction models
Jiarpakdee, J., Tantithamthavorn, C., Dam, H. K. & Grundy, J., 23 Mar 2020, (Accepted/In press) In : IEEE Transactions on Software Engineering. 21 p.Research output: Contribution to journal › Article › Research › peer-review
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Predicting defective lines using a model-agnostic technique
Wattanakriengkrai, S., Thongtanunam, P., Tantithamthavorn, C., Hata, H. & Matsumoto, K., 2020, (Accepted/In press) In : IEEE Transactions on Software Engineering. 18 p.Research output: Contribution to journal › Article › Research › peer-review
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JITBot: an explainable Just-In-Time defect prediction bot
Khanan, C., Luewichana, W., Pruktharathikoon, K., Jiarpakdee, J., Tantithamthavorn, C., Choetkiertikul, M., Ragkhitwetsagul, C. & Sunetnanta, T., 2020, Proceedings - 2020 35th IEEE/ACM International Conference on Automated Software Engineering, ASE 2020. Le Goues, C. & Lo, D. (eds.). New York NY USA: IEEE, Institute of Electrical and Electronics Engineers, p. 1336-1339 4 p. 9286007Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
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The impact of automated feature selection techniques on the interpretation of defect models
Jiarpakdee, J., Tantithamthavorn, C. & Treude, C., 1 Aug 2020, In : Empirical Software Engineering. 25, p. 3590–3638 49 p.Research output: Contribution to journal › Article › Research › peer-review
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Mining Software defects: should we consider affected releases?
Yatish, S., Jiarpakdee, J., Thongtanunam, P. & Tantithamthavorn, C., 2019, Proceedings - 2019 IEEE/ACM 41st International Conference on Software Engineering, ICSE 2019. Bultan, T. & Whittle, J. (eds.). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, p. 654-665 12 p. 8811982Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
7 Citations (Scopus)
Prizes
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ARC Discovery Early Career Researchers Award
Tantithamthavorn, Chakkrit (Recipient), 2 Mar 2020
Prize: Prize (including medals and awards)
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JSPS Research Fellowship
Tantithamthavorn, Chakkrit (Recipient), 1 Apr 2014
Prize: Competitive Fellowships
Activities
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IEEE Transactions on Software Engineering (Journal)
Chakkrit Tantithamthavorn (Peer reviewer)
2020Activity: Publication peer-review and editorial work types › Peer review responsibility
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IEEE Transactions on Software Engineering (Journal)
Chakkrit Tantithamthavorn (Peer reviewer)
1 May 2016Activity: Publication peer-review and editorial work types › Peer review responsibility
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Empirical Software Engineering (Journal)
Chakkrit Tantithamthavorn (Peer reviewer)
1 Dec 2016Activity: Publication peer-review and editorial work types › Editorial responsibility
Press / Media
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How data helps doctors make life-saving decisions in emergency care
16/11/20
1 Media contribution
Press/Media: Article/Feature
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JITBot: An Explainable Just-In-Time Defect Prediction Bot
22/09/20
1 Media contribution
Press/Media: Article/Feature