The accuracy of the floating-point calculation is critical to many applications and different
methods have been proposed around floating-point accuracies, such as detecting the errors …

This paper tackles the important, difficult problem of detecting program inputs that trigger
large floating-point errors in numerical code. It introduces a novel, principled dynamic …

Programs using floating-point arithmetic are prone to accuracy problems caused by
rounding and catastrophic cancellation. These phenomena provoke bugs that are …

Floating-point arithmetic is notorious for its rounding errors, which can propagate and
accumulate, leading to unacceptable results. Detecting inputs that can trigger significant …

F1oating-point numbers use limited precision to represent real numbers and have rounding
errors, so floating-point calculations are inherently inaccurate. Revealing high floating-point …

Tools that automatically prove the absence or detect the presence of large floating-point
roundoff errors or the special values NaN and Infinity greatly help developers to reason …

The machine representation of floating point values has limited precision such that errors
may be introduced during execution. These errors may get propagated and magnified by the …

Given the variety of numerical errors that can occur, floating-point programs are difficult to
write, test and debug. One common practice employed by developers without an advanced …

Floating point representation has limited precision and inputs to floating point programs may
also have errors. Consequently, during execution, errors are introduced, propagated, and …

Floating point is widely used in software to emulate arithmetic over reals. Unfortunately,
floating point leads to rounding errors that propagate and accumulate during execution …