PRINS: Scalable Model Inference for Component-based System Logs

TL;DR

PRINS is a scalable approach for inferring behavioral models from large component-based system logs by dividing the task into component-level inference and merging, significantly improving processing speed while maintaining accuracy.

Contribution

The paper introduces PRINS, a divide-and-conquer model inference technique that scales to large logs and accurately captures system behavior without extra information.

Findings

PRINS processes large logs faster than existing tools.

PRINS maintains high accuracy in inferred models.

Scalability demonstrated on diverse datasets.

Abstract

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unfortunately, these models either are not available during software development or, if available, quickly become outdated as implementations evolve.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.