QuASoQ 2018

6th International Workshop on Quantitative Approaches to Software Quality

in conjunction with the 25th Asia-Pacific Software Engineering Conference (APSEC 2018)
Nara, Japan, 4th December 2018

Key Note by Hongyu Zhang

Intelligent Fault Diagnosis and Prediction through Data Analytics


Although a range of quality assurance measures have been taken, in reality released software and service systems could still contain faults and fail in operation. In the era of big data and artificial intelligence, we aim towards intelligent, data-driven fault diagnosis and prediction. During the development and maintenance of software and services, a vast amount of data is generated. These data include operation logs, historical failures, metrics, etc. Various machine learning and data analytics techniques can be utilized to mine these data to predict failures, prioritize testing resources, and automate fault diagnosis. As a result, software/service reliability and availability could be improved. In this talk, I will briefly introduce some of my recent work on data-driven fault diagnosis and prediction.


Hongyu Zhang is currently an Associate Professor at The University of Newcastle, Australia. Previously, he was a Lead Researcher at Microsoft Research Asia and an Associate Professor at Tsinghua University, China. He received his PhD degree from National University of Singapore in 2003. His research is in the area of Software Engineering, in particular, software analytics, testing, maintenance, and reuse. The main theme of his research is to improve software quality and productivity by mining software data. He has published more than 120 research papers in international journals and conferences, including TSE, TOSEM, ICSE, FSE, POPL, AAAI, KDD, IJCAI, ASE, ISSTA, ICSM, ICDM, and USENIX. He received two ACM Distinguished Paper awards. He also served as a program chair and committee member for many software engineering conferences. He is on the Editorial Board of Journal of Systems and Software, and is a Senior Member of IEEE. More information about him can be found at: https://sites.google.com/site/hongyujohn/