FuSeBMC v4: Improving Code Coverage with Smart Seeds via BMC, Fuzzing and Static Analysis
Kaled M. Alshmrany, Mohannad Aldughaim, Ahmed Bhayat, Lucas Carvalho Cordeiro · Formal Aspects of Computing · 2024
Bounded model checking (BMC) and fuzzing techniques are among the most effective methods for detecting errors and security vulnerabilities in software. However, there are still shortcomings in detecting these errors due to the inability of existing methods to cover large areas in target code. We proposeFuSeBMCv4, a test generator that synthesizes seeds with useful properties, that we refer to assmart seeds, to improve the performance of its hybrid fuzzer thereby achieving high C program coverage.FuSeBMCworks by first analyzing and incrementally injecting goal labels into the given C program to guide BMC and Evolutionary Fuzzing engines. After that, the engines are employed for an initial period to produce the so–called smart seeds. Finally, the engines are run again, with these smart seeds as starting seeds, in an attempt to achieve maximum code coverage/find bugs. During seed generation and normal running, theTracersubsystem aids coordination between the engines. This subsystem conducts additional coverage analysis and updates a shared memory with information on goals covered so far. Furthermore, theTracerevaluates test-cases dynamically to convert cases into seeds for subsequent test fuzzing. Thus, the BMC engine can provide the seed that allows the fuzzing engine to bypass complex mathematical guards (e.g., input validation). As a result, we received three awards for participation in the fourth international competition in software testing (Test-Comp 2022), outperforming all state-of-the-art tools in every category, including the coverage category.