Zuohua Ding

Reliability is an essential software quality requirement, especially for online service software. Without an accurate prediction of service reliability, any unexpected failure can disrupt a service. The majority of existing models use static data collected prior to the release of the software. These types of models may predict the reliability of the software as it was during the data collection phase. However, online service software is continuously evolving, and their behaviors can be changed by the runtime usage. Thus, the prediction made by static data can be inaccurate. We present a method to tackle this challenge by taking into account software runtime behavior in our reliability prediction. We use a data mining tool, Daikon, to collect likely invariants of the software to capture its states in the runtime. This runtime information is then used to compute the reliability of the software by using our port-based reliability model.

Biography: Prof. Zuohua Ding received the M.S. degree in computer science and the Ph.D. degree in mathematics in 1996, both from the University of South Florida, Tampa. He is currently a Professor and the Director with the Center of Intelligent Computing and Software Engineering, Zhejiang Sci-Tech University, Hangzhou, China, and he has been a Research Professor with the National Institute for Systems Test and Productivity, University of South Florida, since 2001. From 1998 to 2001, he was a Senior Software Engineer with Advanced Fiber Communication. Now he is a visiting Professor at UCLA. His research interests include software testing, program modeling and analysis, service computing, and artificial intelligence, subjects on which he has authored and coauthored more than 70 papers.