Rule-based specification mining leveraging learning to rank
Software systems are often released without formal specifications. To deal with the problem of lack of and outdated specifications, rule-based specification mining approaches have been proposed. These approaches analyze execution traces of a system to infer the rules that characterize the protocols, typically of a library, that its clients must obey. Rule-based specification mining approaches work by exploring the search space of all possible rules and use interestingness measures to differentiate specifications from false positives. Previous rule-based specification mining approaches often rely on one or two interestingness measures, while the potential benefit of combining multiple available interestingness measures is not yet investigated. In this work, we propose a learning to rank based approach that automatically learns a good combination of 38 interestingness measures. Our experiments show that the learning to rank based approach outperforms the best performing approach leveraging single interestingness measure by up to 66%.
Thu 14 Nov
13:40 - 15:20: Papers - Mining and Bug Detection at Cortez 2&3 Chair(s): Chanchal K. RoyUniversity of Saskatchewan | ||||||||||||||||||||||||||||||||||||||||||
13:40 - 14:00 Talk | Automatically 'Verifying' Complex Systems through Learning, Abstraction and Refinement Jingyi WangNational University of Singapore, Singapore, Jun SunSingapore Management University, Singapore, Shengchao QinUniversity of Teesside, Cyrille JegourelISTD, Singapore University of Technology and Design Link to publication | |||||||||||||||||||||||||||||||||||||||||
14:00 - 14:20 Talk | Interactive semi-automated specification mining for debugging: An experience report Mohammad Jafar MashhadiUniversity of Calgary, Taha R. SiddiquiInfoMagnetics Technologies Corp, Hadi HemmatiUniversity of Calgary, Howard W. LoewenDepartment of Electrical & Computer Engineering, University of Calgary Link to publication | |||||||||||||||||||||||||||||||||||||||||
14:20 - 14:40 Talk | Improving reusability of software libraries through usage pattern mining Mohamed Aymen SaiedConcordia University, Ali OuniETS Montreal, University of Quebec, Houari SahraouiUniversité de Montréal, Raula Gaikovina KulaNAIST, Katsuro InoueOsaka University, David LoSingapore Management University Link to publication | |||||||||||||||||||||||||||||||||||||||||
14:40 - 15:00 Talk | Rule-based specification mining leveraging learning to rank Zherui CaoZhejiang University, Yuan TianQueens University, Kingston, Canada, Tien-Duy B. LeSchool of Information Systems, Singapore Management University, David LoSingapore Management University Link to publication | |||||||||||||||||||||||||||||||||||||||||
15:00 - 15:10 Demonstration | TsmartGP: A Tool for Finding Memory Defects with Pointer Analysis Yuexing WangTsinghua University, Guang ChenTsinghua University, Min ZhouTsinghua University, Ming GuTsinghua University, Jiaguang SunTsinghua University | |||||||||||||||||||||||||||||||||||||||||
15:10 - 15:20 Demonstration | Ares: Inferring Error Specifications through Static Analysis Li ChiTsinghua University, Zuxing GuSchool of Software, Tsinghua University, Min ZhouTsinghua University, Ming GuTsinghua University, Hongyu ZhangThe University of Newcastle |