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, eg, 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, any of whose …

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, eg, 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, any of whose …