Behavioral software models play a key role in many software engineering tasks; unfortunately, these models either are not available during software development or, if available, quickly become outdated as implementations evolve. Model inference techniques have been proposed as a viable solution to extract finite state models from execution logs. However, existing techniques do not scale well when processing very large logs that can be commonly found in practice.

In this paper, we address the scalability problem of inferring the model of a component-based system from large system logs, without requiring any extra information. Our model inference technique, called \textit{PRINS}, follows a divide-and-conquer approach. The idea is to first infer a model of each system component from the corresponding logs; then, the individual component models are merged together taking into account the flow of events across components, as reflected in the logs. We evaluated \textit{PRINS} in terms of scalability and accuracy, using nine datasets composed of logs extracted from publicly available benchmarks and a personal computer running desktop business applications. The results show that \textit{PRINS} can process large logs much faster than a publicly available and well-known state-of-the-art tool, without significantly compromising the accuracy of inferred models.