Efficient Safety Proofs for Industry-Scale Code Using Abstractions and Bounded Model Checking
Priyanka Darke, Bharti Chimdyalwar, Avriti Chauhan, R. Venkatesh · 2017
Loop Abstraction followed by Bounded Model Checking, or LABMC in short, is a promising recent technique for proving safety of large programs. In an experimental setup proposed last year [14], LABMC was combined with slicing and Iterative Context Extension (ICE) with the aim of achieving scalability over industrial code. In this paper, we address two major limitations of that set-up, namely (i) the inability of ICE to prune redundant code in a verification context, and (ii) the unavailability of a tool that implements the set-up. We propose an improvement over ICE called Iterative Function Level Slicing (IFLS) and incorporate it in our tool called ELABMC, to offer an efficient implementation of [14]. We substantiate our claim with two sets of experiments over industrial applications as well as academic benchmarks. Quantifying the benefits of IFLS over traditional ICE in one, our results report that IFLS leads to 34.9% increase in efficiency, 17.7% improvement in precision, and scales in 14.2% more cases. With the second experiment, we show that ELABMC outperforms state-of-the-art verification techniques in the task of identifying static analysis warnings as false alarms.