- author = "Michael Foster and John Derrick and Neil Walkinshaw",
- title = "Reverse-Engineering {EFSMs} with Data Dependencies",
- booktitle = "33rd IFIP International Conference on Testing Software and Systems",
- year = "2021",
- editor = "David Clark and Hector Menendez and Ana Rosa Cavalli",
- volume = "13045",
- series = "Lecture Notes in Computer Science",
- pages = "37--54",
- address = "virtual",
- month = "10-11 " # nov,
- keywords = "genetic algorithms, genetic programming, SBSE, EFSM Inference, Model Inference",
- isbn13 = "978-3-031-04673-5",
-
URL = "
https://eprints.whiterose.ac.uk/177494/",
-
DOI = "
doi:10.1007/978-3-031-04673-5_3",
- size = "16 pages",
- abstract = "EFSMs provide a way to model systems with internal data variables. In situations where they do not already exist, we need to infer them from system behaviour. A key challenge here is inferring the functions which relate inputs, outputs, and internal variables. Existing approaches either work with white-box traces, which expose variable values, or rely upon the user to provide heuristics to recognise and generalise particular data-usage patterns. This paper presents a preprocessing technique for the inference process which generalises the concrete values from the traces into symbolic functions which calculate output from input, even when this depends on values not present in the original traces. Our results show that our technique leads to more accurate models than are produced by the current state-of-the-art and that somewhat accurate models can still be inferred even when the output of particular transitions depends on values not present in the original traces.",
-
notes = "Published 10 May 2022. Published in cooperation with
http://www.ifip.org/ Department of Computer Science,
The University of Sheffield, Regent Court, Sheffield,
S1 4DP, UK
http://ictss2021.cs.ucl.ac.uk/",
