AutoStub: Genetic Programming-Based Stub Creation for Symbolic Execution
This addresses a bottleneck in symbolic execution for software testers by automating stub creation, though it is an incremental improvement over existing manual or context-dependent methods.
The paper tackles the problem of external functions limiting symbolic execution in software testing by proposing AutoStub, a method that automatically generates symbolic stubs using Genetic Programming, achieving over 90% accuracy for 55% of evaluated functions and enabling exploration of previously intractable paths.
Symbolic execution is a powerful technique for software testing, but suffers from limitations when encountering external functions, such as native methods or third-party libraries. Existing solutions often require additional context, expensive SMT solvers, or manual intervention to approximate these functions through symbolic stubs. In this work, we propose a novel approach to automatically generate symbolic stubs for external functions during symbolic execution that leverages Genetic Programming. When the symbolic executor encounters an external function, AutoStub generates training data by executing the function on randomly generated inputs and collecting the outputs. Genetic Programming then derives expressions that approximate the behavior of the function, serving as symbolic stubs. These automatically generated stubs allow the symbolic executor to continue the analysis without manual intervention, enabling the exploration of program paths that were previously intractable. We demonstrate that AutoStub can automatically approximate external functions with over 90% accuracy for 55% of the functions evaluated, and can infer language-specific behaviors that reveal edge cases crucial for software testing.