Configurable Software System

Configurable software systems, designed to adapt to diverse needs and environments, are a focus of intense research aiming to optimize their performance, reliability, and usability. Current efforts concentrate on developing robust prediction models, often employing deep learning architectures like neural networks and Bayesian methods, to anticipate performance under various configurations and environments, and on improving the efficiency of configuration verification and error localization using techniques like causal inference and large language models. This research is crucial for enhancing the development and deployment of complex systems across numerous domains, from autonomous driving to high-performance computing, by enabling more efficient resource allocation, improved fault tolerance, and ultimately, better user experiences.