Pitfalls in applying model learning to industrial legacy software
conference paper
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.
Topics
TNO Identifier
843701
ISSN
03029743
ISBN
9783030034269
Publisher
Springer Verlag
Source title
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
Editor(s)
Margaria T.
Steffen B.
Steffen B.
Pages
121-138
Files
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