Chakkrit Tantithamthavorn

Dr

Accepting PhD Students

PhD projects

Practical and Explainable AI-Driven Defect Detection, Software Engineering for AI-enabled Systems, Advancing Agile Testing Practices

20122020

Research activity per year

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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

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