Combining Model Checking and Data-Flow Analysis

Until recently, model checking and data-flow analysis — two traditional approaches to software verification — were used independently and in isolation for solving similar problems. Theoretically, the two different approaches are equivalent; they are two different ways to compute the same solution to a problem. In recent years, new practical approaches have shown how to combine the approaches and how to make them benefit from each other — model-checking techniques can make data-flow analyses more precise, and data-flow-analysis techniques can make model checking more efficient. This chapter starts by discussing the relationship (differences and similarities) between type checking, data-flow analysis, and model checking. Then we define algorithms for data-flow analysis and model checking in the same formal setting, called configurable program analysis. This identifies key differences that make us call an algorithm a “model-checking” algorithm or a “data-flow-analysis” algorithm. We illustrate the effect of using different algorithms for running certain classic example analyses and point out the reason for one algorithm being “better” than the other. The chapter presents combined verification techniques in the framework of configurable program analysis, in order to emphasize techniques used in data-flow analysis and in model checking. Besides the iterative algorithm that is used to illustrate the similarities and differences between data-flow analysis and model checking, we discuss different algorithmic approaches for constructing program invariants. To show that the border between data-flow analysis and model checking is blurring and disappearing, we also discuss directions in tool implementations for combined verification approaches. Dirk Beyer Ludwig-Maximilians-Universität München, Munich, Germany Sumit Gulwani Microsoft Research, Redmond, WA, USA David A. Schmidt Kansas State University, Manhattan, KS, USA