Title
Pitfalls in applying model learning to industrial legacy software
Author
al Duhaiby, O.
Mooij, A.J.
van Wezep, H.
Groote, J.F.
Contributor
Margaria, T. (editor)
Steffen, B. (editor)
Publication year
2018
Abstract
Maintaining legacy software is one of the most common struggles of the software industry, being costly yet essential. We tackle that problem by providing better understanding of software by extracting behavioural models using the model learning technique. The used technique interacts with a running component and extracts abstract models that would help developers make better informed decisions. As promising in theory, as slippery in application it is, however. This report describes our experience in applying model learning to legacy software, and aims to prepare the newcomer for what shady pitfalls lie therein as well as provide the seasoned researcher with concrete cases and open problems. We narrate our experience in analysing certain legacy components at Philips Healthcare describing challenges faced, solutions implemented, and lessons learned. © Springer Nature Switzerland AG 2018.
Subject
Active learning
Legacy software
Model learning
Formal methods
Abstract models
Active Learning
Behavioural model
Informed decision
Legacy component
Legacy software
Model learning
Software industry
Legacy systems
To reference this document use:
http://resolver.tudelft.nl/uuid:2f2d2254-1ffa-485f-82a4-7b0f62ac9ba1
TNO identifier
843701
Publisher
Springer Verlag
ISBN
9783030034269
ISSN
0302-9743
Source
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) : 8th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation, ISoLA 2018, 5 November 2018 through 9 November 2018, 11247 LNCS, 121-138
Bibliographical note
Conference Paper
Document type
conference paper