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Bounded model checking (BMC) and fuzzing techniques are among the mosteffective methods for detecting errors and security vulnerabilities insoftware. However, there are still shortcomings in detecting these errors dueto the inability of existent methods to cover large areas in target code. Wepropose FuSeBMC v4, a test generator that synthesizes seeds with usefulproperties, that we refer to as smart seeds, to improve the performance of itshybrid fuzzer thereby achieving high C program coverage. FuSeBMC works by firstanalyzing and incrementally injecting goal labels into the given C program toguide BMC and Evolutionary Fuzzing engines. After that, the engines areemployed 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 anattempt to achieve maximum code coverage / find bugs. During both seedgeneration and normal running, coordination between the engines is aided by theTracer subsystem. This subsystem carries out additional coverage analysis andupdates a shared memory with information on goals covered so far. Furthermore,the Tracer evaluates test cases dynamically to convert cases into seeds forsubsequent test fuzzing. Thus, the BMC engine can provide the seed that allowsthe fuzzing engine to bypass complex mathematical guards (e.g., inputvalidation). As a result, we received three awards for participation in thefourth international competition in software testing (Test-Comp 2022),outperforming all state-of-the-art tools in every category, including thecoverage category.

FuSeBMC v4: Improving code coverage with smart seeds via BMC, fuzzing and static analysis