Heuristic-based Mining of Service Behavioral Models from Interaction Traces

TL;DR

This paper introduces a heuristic-based method for inferring accurate, concise behavioral models from interaction traces, significantly improving precision and recall over existing techniques with minimal computational cost.

Contribution

It proposes a novel heuristic-based generalization and truthful minimization approach for more accurate behavioral model inference from interaction traces.

Findings

Achieves 100% precision and recall in model inference.

Outperforms state-of-the-art techniques in accuracy.

Maintains limited computational overhead.

Abstract

Software behavioral models have proven useful for emulating and testing software systems. Many techniques have been proposed to infer behavioral models of software systems from their interaction traces. The quality of the inferred model is critical to its successful use. While generalization is necessary to deduce concise behavioral models, existing techniques of inferring models, in general, overgeneralize what behavior is valid. Imprecise models include many spurious behaviors, and thus compromise the effectiveness of their use. In this paper, we propose a novel technique that increases the accuracy of the behavioral model inferred from interaction traces. The essence of our approach is a heuristic-based generalization and truthful minimization. The set of heuristics include patterns to match input traces and generalize them towards concise model representations. Furthermore, we adopt a truthful minimization technique to merge these generalized traces. The key insight of our approach is to infer a concise behavioral model without compromising its accuracy. We present an empirical evaluation of how our approach improves upon the state-of-the-art specification inference techniques. The results show that our approach mines model with 100% precision and recall with a limited computation overhead.