20122019
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Personal profile

Biography

He is a lecturer (a.k.a. Assistant Professor) in Faculty of Information Technology, Monash University. My current research focuses on AI-Driven Software Quality Assurance in the Age of DevOps. Prior to that, he was a lecturer at the University of Adelaide, a research fellow at Queen's University, a JSPS fellow at Nara Institute of Science and Technology. His research has been published at top-tier software engineering venues, such as IEEE Transactions on Software Engineering (TSE), Empirical Software Engineering (EMSE), and the International Conference on Software Engineering (ICSE). During his Ph.D. study, he won 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, and won a "Best Ph.D. Student Award".

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.

Keywords

  • Software Engineering
  • Empirical Software Engineering
  • Software Quality Assurance
  • Machine Learning
  • Statistical and Data Analysis
  • Deep Learning for Cyber Security
  • Artificial intelligence

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Research Output 2012 2019

The impact of automated parameter optimization on defect prediction models

Tantithamthavorn, C., McIntosh, S., Hassan, A. E. & Matsumoto, K., 2019, (Accepted/In press) In : IEEE Transactions on Software Engineering. 32 p.

Research output: Contribution to journalArticleResearchpeer-review

The impact of class rebalancing techniques on the performance and interpretation of defect prediction models

Tantithamthavorn, C., Hassan, A. E. & Matsumoto, K., 2019, (Accepted/In press) In : IEEE Transactions on Software Engineering. 22 p.

Research output: Contribution to journalArticleResearchpeer-review

An experience report on defect modelling in practice: pitfalls and challenges

Tantithamthavorn, C. & Hassan, A. E., 2018, Proceedings - 2018 ACM/IEEE 40th International Conference on Software Engineering: Software Engineering in Practice - ICSE-SEIP 2018: 30 May – 1 June 2018 Gothenburg, Sweden. Crnkovic, I. (ed.). New York NY USA: IEEE, Institute of Electrical and Electronics Engineers, p. 286-295 10 p.

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Artefact: an R implementation of the AutoSpearman function

Jiarpakdee, J., Tantithamthavorn, C. & Treude, C., 2018, Proceedings - 2018 IEEE International Conference on Software Maintenance and Evolution - ICSME 2018: 23–29 September 2018 Madrid, Spain. Khomh, F. & Lo, D. (eds.). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, p. 711 1 p. 8530086

Research output: Chapter in Book/Report/Conference proceedingConference PaperOtherpeer-review

AutoSpearman: automatically mitigating correlated software metrics for interpreting defect models

Jiarpakdee, J., Tantithamthavorn, C. & Treude, C., 2018, Proceedings - 2018 IEEE International Conference on Software Maintenance and Evolution - ICSME 2018: 23–29 September 2018 Madrid, Spain. Khomh, F. & Lo, D. (eds.). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, p. 92-103 12 p. 8530020

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Prizes

JSPS Research Fellowship

Chakkrit Tantithamthavorn (Recipient), 1 Apr 2014

Prize: Competitive Fellowships

Activities 2016 2016

  • 1 Editorial responsibility
  • 1 Peer review responsibility

IEEE Transactions on Software Engineering (Journal)

Chakkrit Tantithamthavorn (Peer reviewer)
1 May 2016

Activity: Publication peer-review and editorial work typesPeer review responsibility

Empirical Software Engineering (Journal)

Chakkrit Tantithamthavorn (Peer reviewer)
1 Dec 2016

Activity: Publication peer-review and editorial work typesEditorial responsibility