<strong>Predictive Mutation Testing</strong>

<strong>Predictive Mutation Testing</strong>

时间：2016年8月9日上午10:00-11:00 地点：九龙湖校区计算机楼413

报告简介：

Mutation testing is a powerful methodology for evaluating test suite quality. In mutation testing, a large number of mutants are generated and executed against the test suite to check the ratio of killed mutants. Therefore, mutation testing is widely believed to be a computationally expensive technique. To alleviate the efficiency concern of mutation testing, this talk introduces predictive mutation testing (PMT), the first approach to predicting mutation testing results without mutant execution. In particular, the proposed approach constructs a classification model based on a series of features related to mutants and tests, and uses the classification model to predict whether a mutant is killed or survived without executing it. PMT has been evaluated on 163 real-world projects under two application scenarios (i.e., cross-version and cross-project). The experimental results demonstrate that PMT improves the efficiency of mutation testing by up to 151.4X while only incurring a small accuracy loss on mutant execution result prediction, indicating a good tradeoff between efficiency and effectiveness of mutation testing.

报告人简介：

Dr. Lingming Zhang（张令明） is an assistant professor in the Computer Science Department at the University of Texas at Dallas. He obtained his Ph.D. degree from the Department of Electrical and Computer Engineering in the University of Texas at Austin in May 2014. He received his MS degree and BS degree in Computer Science from Peking University (2010) and Nanjing University (2007), respectively. His research interests lie broadly in software engineering and programming languages, including automated program analysis, testing, debugging, and verification, as well as software evolution and mobile computing. He has authored over 30 papers in premier software engineering or programming language transactions and conferences, including ICSE, FSE, ISSTA, ASE, POPL, OOPSLA, TSE and TOSEM. He has also served on the program committee or artifact evaluation committee for various international conferences (including ASE, ICST, ICSM, ISSRE, COMPSAC, QRS, OOPSLA, and ISSTA). His research is being supported by NSF and Google.

计算机网络和信息集成教育部重点实验室（东南大学）