Non-Semantics-Preserving Transformations for Higher-Coverage Test Generation Using Symbolic Execution

Abstract

Symbolic execution is a well-studied method that has a number of useful applications, including generation of high-quality test suites that find many bugs. However, scaling it to real-world applications is a significant challenge, as it depends on the often expensive process of solving constraints on program inputs. Our insight is that when the goal of symbolic execution is test generation, non-semantics-preserving program transformations can reduce the cost of symbolic execution and the tests generated for the transformed programs can still serve as quality suites for the original program. We present five such transformations based on a few different program simplification heuristics that are designed to lower the cost of symbolic execution for input generation. As enabling technology we use the KLEE symbolic execution engine and the LLVM compiler infrastructure. We evaluate our transformations using a suite of small subjects as well as a subset of the well-studied Unix Coreutils. In a majority of cases, our approach reduces the time for symbolic execution for input generation and increases code coverage of the resultant suite.