153 citations · 322 across the 21 of their papers we have counts for
4 papers · 2 filters
Learning Likely Invariants to Explain Why a Program Fails
Jun Sun, Long H. Pham, Lyly Tran Thi +2
Debugging is difficult. Recent studies show that automatic bug localization techniques have limited usefulness. One of the reasons is that programmers typically have to understand…
Automatically 'Verifying' Discrete-Time Complex Systems through Learning, Abstraction and Refinement
Jingyi Wang, Jun Sun, Shengchao Qin +1
Precisely modeling complex systems like cyber-physical systems is challenging, which often render model-based system verification techniques like model checking infeasible. To over…
Towards Concolic Testing for Hybrid Systems
Pingfan Kong, Yi Li, Xiaohong Chen +3
Hybrid systems exhibit both continuous and discrete behavior. Analyzing hybrid systems is known to be hard. Inspired by the idea of concolic testing (of programs), we investigate w…
Should We Learn Probabilistic Models for Model Checking? A New Approach and An Empirical Study
Jingyi Wang, Jun Sun, Qixia Yuan +1
Many automated system analysis techniques (e.g., model checking, model-based testing) rely on first obtaining a model of the system under analysis. System modeling is often done ma…