He is a lecturer in the School of Computer Science, the University of Adelaide (one of the top 125 QS World Universities Ranking, and a Go8 Australian leading university). He pioneered research on the pitfalls of software analytics modelling in order to provide avoidance strategies. 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 the "Best Ph.D. Student Award".

Interests: Big Data Analytics, Data Science, Predictive Modelling in Software Engineering, Empirical Software Engineering, Mining Software Repository, Modern Statistical Analysis

Address: Level 4, Room 47, School of Computer Science, the University of Adelaide, SA, Australia.
Email: chakkrit.tantithamthavorn@adelaide.edu.au
Phone: +61-8-8313-3516

June 08, 2018
I was invited to be a journal referee for Journal of Software: Evolution and Process (Impact Factor: 1.033).
June 06, 2018
Super congrats to Jirayus Jiarpakdee (my first Ph.D. student) on his first accepted paper titled "AutoSpearman: Automatically Mitigating Correlated Software Metrics for Interpreting Defect Models" has been accepted at the the 34rd IEEE International Conference on Software Maintenance and Evolution (ICSME 2018) with an acceptance rate of 26% (37+8/174).
June 05, 2018
I was invited to be a program committee member for Research Track of the International Conference on Software Analysis, Evolution, and Reengineering (SANER 2019).
June 01, 2018
My paper on "The Impact of IR-based Classifier Configuration on the Performance and the Effort of Method-Level Bug Localization" has been accepted at the Elsevier Journal of Information and Software Technology (IST), Impact Factor: 2.694!
May 08, 2018
I was invited to be a program committee member for Research Track of the International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE 2018).
May 07, 2018
I was invited to be a publicity co-chair of the 9th IEEE International Workshop on Empirical Software Engineering in Practice (IWESEP 2018).
February 07, 2018
I was invited to be a program committee member for Research Track of the 34rd IEEE International Conference on Software Maintenance and Evolution (ICSME 2018).
January 24, 2018
My paper on An Experience Report on Defect Modelling in Practice: Pitfalls and Challenges has been accepted at International Conference on Software Engineering (ICSE): SEIP Track, with an acceptance rate of 24% (31/131)!
January 16, 2018
My paper on The Impact of Automated Parameter Optimization on Defect Prediction Models has been accepted at IEEE Transactions on Software Engineering (TSE)!
December 26, 2017
Our EMSE paper on Studying the Dialogue Between Users and Developers of Free Apps in the Google Play Store" is invited to present at ICSE 2018, Gothenburg, Sweden.
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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.