Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/38231
Title: Inferring Computational State Machine Models from Program Executions
Authors: Walkinshaw, Neil
Hall, Mathew
First Published: 16-Jan-2017
Presented at: International Conference on Software Maintenance and Evolution (ICSME) 2016 Oct 5 – Oct 7, Raleigh, North Carolina, USA
Start Date: 2-Oct-2016
End Date: 10-Oct-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: 2016 IEEE International Conference on Software Maintenance and Evolution (ICSME), Raleigh, NC, 2016, pp. 122-132.
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 ma- chines 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.
DOI Link: 10.1109/ICSME.2016.74
Links: http://ieeexplore.ieee.org/document/7816460/
http://hdl.handle.net/2381/38231
Version: Post-print
Status: Peer-reviewed
Type: Conference Paper
Rights: Copyright © 2016, IEEE. Deposited with reference to the publisher’s open access archiving policy.
Appears in Collections:Conference Papers & Presentations, Dept. of Computer Science

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