Walkinshaw, N. and Hall, M. orcid.org/0000-0002-9408-2996 (2017) Inferring Computational State Machine Models from Program Executions. In: 32nd IEEE International Conference On Software Maintenance And Evolution (ICSME). IEEE , pp. 123-133.
Abstract
The challenge of inferring state machines from log data or execution traces is well-established, and has led to the development of several powerful techniques. Current approaches tend to focus on the inference of conventional finite state machines or, in few cases, state machines with guards. However, these machines are ultimately only partial, because they fail to model how any underlying variables are computed during the course of an execution, they are not computational. In this paper we introduce a technique based upon Genetic Programming to infer these data transformation functions, which in turn render inferred automata fully computational. Instead of merely determining whether or not a sequence is possible, they can be simulated, and be used to compute the variable values throughout the course of an execution. We demonstrate the approach by using a Cross-Validation study to reverse-engineer complete (computational) EFSMs from traces of established implementations.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Reverse Engineering; State Machines; Genetic Programming |
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: | 27 Feb 2018 09:46 |
Last Modified: | 10 Apr 2018 15:35 |
Published Version: | https://doi.org/10.1109/ICSME.2016.74 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/ICSME.2016.74 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:127869 |