Tuning parallel symbolic execution engine for better performance

Abstract

Symbolic execution is widely used in many code analysis, testing, and verification tools. As symbolic execution exhaustively explores all feasible paths, it is quite time consuming. To handle the problem, researchers have paralleled existing symbolic execution tools (e.g., KLEE). In particular, Cloud9 is a widely used paralleled symbolic execution tool, and researchers have used the tool to analyze real code. However, researchers criticize that tools such as Cloud9 still cannot analyze large scale code. In this paper, we conduct a field study on Cloud9, in which we use KLEE and Cloud9 to analyze benchmarks in C. Our results confirm the criticism. Based on the results, we identify three bottlenecks that hinder the performance of Cloud9: the communication time gap, the job transfer policy, and the cache management of the solved constraints. To handle these problems, we tun the communication time gap with better parameters, modify the job transfer policy, and implement an approach for cache management of solved constraints. We conduct two evaluations on our benchmarks and a real application to understand our improvements. Our results show that our tuned Cloud9 reduces the execution time significantly, both on our benchmarks and the real application. Furthermore, our evaluation results show that our tuning techniques improve the effectiveness on all the devices, and the improvement can be achieved upto five times, depending upon a tuning value of our approach and the behaviour of program under test.

Keywords

code analysis / symbolic execution / parallelizing symbolic execution / KLEE / Cloud9

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