Shin, D. orcid.org/0000-0002-0840-6449, Bianculli, D. and Briand, L. (2022) PRINS : scalable model inference for component-based system logs. Empirical Software Engineering, 27 (4). 87. ISSN 1382-3256
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unfortunately, these models either are not available during software development or, if available, quickly become outdated as implementations evolve.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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
Keywords: | Logs; Model inference; Component-based system |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 31 Aug 2022 08:01 |
Last Modified: | 02 Sep 2022 02:58 |
Status: | Published |
Publisher: | Springer Nature |
Refereed: | Yes |
Identification Number: | 10.1007/s10664-021-10111-4 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:189630 |
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