Achieving high code coverage is essential in testing, which gives us confidence in code quality. Testing floating-point code usually requires painstaking efforts in handling floating-point constraints, e.g., in symbolic execution. This paper turns the challenge of testing floating-point code into the opportunity of applying unconstrained programming – the mathematical solution for calculating function minimum points over the entire search space. Our core insight is to derive a representing function from the floating-point program, whose minimum point is a test input guaranteed to exercise a new branch of the tested program. This guarantee allows us to achieve high coverage of the floating-point program by repeatedly minimizing the representing function.

We have implemented this approach into the tool CoverMe and conducted an extensive evaluation on Sun’s C math library. Our experiment shows that CoverMe can achieve 90.8% of branch coverage in 6.9 seconds on average. This result dramatically outperforms our compared tools: (1) Random testing, (2) AFL, a highly optimized, robust fuzzer released by Google, and (3) Austin, a state-of-the-art coverage-based testing tool designed for supporting floating-point code.