- author = "Felix Maechtle and Nils Loose and Jan-Niclas Serr and Jonas Sander and Thomas Eisenbarth",
- title = "{AutoStub}: Genetic Programming-Based Stub Creation for Symbolic Execution",
- booktitle = "Proceedings of the 18th ACM/IEEE International Workshop on Search-Based and Fuzz Testing, SBFT 2025",
- year = "2025",
- editor = "Gunel Jahangirova and Matteo Biagiola and Vincenzo Riccio",
- address = "Ottawa",
- month = "28-29 " # apr,
- publisher = "IEEE",
- keywords = "genetic algorithms, genetic programming, grammar guided",
-
code_url = "
https://github.com/UzL-ITS/AutoStub",
- abstract = "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. 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.",
-
notes = "Also known as \cite{machtle2024autostub}
SBFT 2025 https://sbft25.github.io/",
