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 …

Code coverage analysis plays an important role in the software testing process. More
recently, the remarkable effectiveness of coverage feedback has triggered a broad interest …

Programs with floating-point computations are often derived from mathematical models or
designed with the semantics of the real numbers in mind. However, for a given input, the …

Coverage analysis is widely used but can suffer from high overhead. This overhead is
especially acute in the context of Python, which is already notoriously slow (a recent study …

Programs with floating-point computations are often derived from mathematical models or
designed with the semantics of the real numbers in mind. However, for a given input, the …

Software testing is widely used in industry, but its application in the High Performance
Computing area has been scarce. Concolic testing, that automates testing via generation of …

This work studies the connection between the problem of analyzing floating-point code and
that of function minimization. It formalizes this connection as a reduction theory, where the …

In recent years, fuzz testing has proven itself to be one of the most effective techniques for
finding correctness bugs and security vulnerabilities in practice. One particular fuzz testing …

For numerical programs, or more generally for programs with floating-point data, it may be
that large savings of time and storage are made possible by using numerical maximization …

High-coverage testing is challenging. Modern object-oriented programs present additional
challenges for testing. One key difficulty is the generation of proper method sequences to …