Clun, D., Shin, D. orcid.org/0000-0002-0840-6449, Filieri, A. et al. (1 more author) (2024) Rigorous assessment of model inference accuracy using language cardinality. ACM Transactions on Software Engineering and Methodology, 33 (4). 95. pp. 1-39. ISSN 1049-331X
Abstract
Models such as finite state automata are widely used to abstract the behavior of software systems by capturing the sequences of events observable during their execution. Nevertheless, models rarely exist in practice and, when they do, get easily outdated; moreover, manually building and maintaining models is costly and error-prone. As a result, a variety of model inference methods that automatically construct models from execution traces have been proposed to address these issues.
However, performing a systematic and reliable accuracy assessment of inferred models remains an open problem. Even when a reference model is given, most existing model accuracy assessment methods may return misleading and biased results. This is mainly due to their reliance on statistical estimators over a finite number of randomly generated traces, introducing avoidable uncertainty about the estimation and being sensitive to the parameters of the random trace generative process.
This paper addresses this problem by developing a systematic approach based on analytic combinatorics that minimizes bias and uncertainty in model accuracy assessment by replacing statistical estimation with deterministic accuracy measures. We experimentally demonstrate the consistency and applicability of our approach by assessing the accuracy of models inferred by state-of-the-art inference tools against reference models from established specification mining benchmarks.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 Copyright held by the owner/author(s). This is an author-produced version of a paper subsequently published in ACM Transactions on Software Engineering and Methodology. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Model inference; specification mining; process mining; model assessment; formal specifications; machine learning; software engineering; behavioral comparison; conformance checking; precision; recall |
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: | 02 Feb 2024 10:54 |
Last Modified: | 06 Nov 2024 14:31 |
Status: | Published |
Publisher: | Association for Computing Machinery (ACM) |
Refereed: | Yes |
Identification Number: | 10.1145/3640332 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:208612 |