PRINS: Scalable Model Inference for Component-based System Logs

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or, if available, quickly become outdated as implementations evolve.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 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 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
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.