Compositional Verification of Heap-Manipulating Programs through Property-Guided Learning
Analyzing and verifying heap-manipulating programs automatically is challenging. A key for fighting the complexity is to develop compositional methods. For instance, existing verifiers for heap-manipulating programs require user-provided specification for each function in the program in order to decompose the verification problem. The requirement, however, often hinders the users from applying such tools. To overcome the issue, we propose to automatically learn heap-related program invariants in a property-guided way for each function call. The invariants are learned based on the memory graphs observed during test execution and improved through memory graph mutation. We implemented a prototype of our approach and integrated it with two existing program verifiers. The experimental results show that our approach enhances existing verifiers effectively in automatically verifying complex heap-manipulating programs with multiple function calls.
Tue 3 Dec
|15:30 - 15:45|
Yu-Fang ChenAcademia Sinica, Chang-Yi ChiangGraduate Institute of Information Management, National Taipei University, Taiwan, Lukas HolikBrno University of Technology, Wei-Tsung KaoInstitute of Information Science, Academia Sinica, Taiwan, Hsin-Hung LinInstitute of Information Science, Academia Sinica, Taiwan, Yean-Fu WenGraduate Institute of Information Management, National Taipei University, Taiwan, Tomas VojnarBrno University of Technology, Wei-Cheng WuInstitute of Information Science, Academia Sinica, Taiwan
|15:45 - 16:15|
|16:15 - 16:45|
|16:45 - 17:15|