Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

4-2017

Abstract

Many automated system analysis techniques (e.g., model checking, model-based testing) rely on first obtaining a model of the system under analysis. System modeling is often done manually, which is often considered as a hindrance to adopt model-based system analysis and development techniques. To overcome this problem, researchers have proposed to automatically “learn” models based on sample system executions and shown that the learned models can be useful sometimes. There are however many questions to be answered. For instance, how much shall we generalize from the observed samples and how fast would learning converge? Or, would the analysis result based on the learned model be more accurate than the estimation we could have obtained by sampling many system executions within the same amount of time? In this work, we investigate existing algorithms for learning probabilistic models for model checking, propose an evolution-based approach for better controlling the degree of generalization and conduct an empirical study in order to answer the questions. One of our findings is that the effectiveness of learning may sometimes be limited.

Keywords

Genetic algorithm, Model learning, Probabilistic model checking

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Fundamental Approaches to Software Engineering: 20th International Conference, FASE 2017, Uppsala, Sweden, April 22-29: Proceedings

Volume

10202

First Page

3

Last Page

21

ISBN

9783662544938

Identifier

10.1007/978-3-662-54494-5_1

Publisher

Springer

City or Country

Cham

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1007/978-3-662-54494-5_1

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