Zhanwei ZHANG

Employing an extensive flight tracking data set (Flightradar24), we assess the interflight variability in CO2 emissions from global aviation with unprecedented spatial and temporal granularity. We develop reduced-form models for over two hundred standard aircraft types that capture flight-to-flight variability.

We propose MGDE, a novel method to detect error-inducing inputs based on mathematical guidance using the Newton-Raphson method with quadratic convergence properties. Our method identifies 89 bugs across 44 programs while being 6.4x faster than the baseline.

We propose PI-detector, a novel approach to computing floating-point errors effectively and efficiently by injecting small perturbations into the operands of individual atomic operations within the program and comparing the outcomes of the original program with the perturbed version.

We propose DELA, an approach based on the observation that most numerical errors stem from large condition numbers in atomic operations. DELA injects small perturbations into the results of individual atomic operations and compares the outcomes to detect errors.