Pitfalls in applying model learning to industrial legacy software

Omar al Duhaiby, Arjan Mooij, Hans van Wezep, Jan Friso Groote

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

1 Citaat (Scopus)

Uittreksel

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.

TaalEngels
TitelLeveraging Applications of Formal Methods, Verification and Validation. Industrial Practice - 8th International Symposium, ISoLA 2018, Proceedings
RedacteurenTiziana Margaria, Bernhard Steffen
Plaats van productieCham
UitgeverijSpringer
Pagina's121-138
Aantal pagina's18
ISBN van elektronische versie978-3-030-03427-6
ISBN van geprinte versie978-3-030-03426-9
DOI's
StatusGepubliceerd - 30 okt 2018
Evenement8th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation, (ISoLA 2018) - Limassol, Cyprus
Duur: 5 nov 20189 nov 2018
http://www.isola-conference.org/isola2018/

Publicatie series

NaamLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11247 LNCS
ISSN van geprinte versie0302-9743
ISSN van elektronische versie1611-3349

Congres

Congres8th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation, (ISoLA 2018)
Verkorte titelISoLA2018
LandCyprus
StadLimassol
Periode5/11/189/11/18
Internet adres

Vingerafdruk

Software
Model
Healthcare
Open Problems
Industry
Learning
Experience

Trefwoorden

    Citeer dit

    al Duhaiby, O., Mooij, A., van Wezep, H., & Groote, J. F. (2018). Pitfalls in applying model learning to industrial legacy software. In T. Margaria, & B. Steffen (editors), Leveraging Applications of Formal Methods, Verification and Validation. Industrial Practice - 8th International Symposium, ISoLA 2018, Proceedings (blz. 121-138). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11247 LNCS). Cham: Springer. DOI: 10.1007/978-3-030-03427-6_13
    al Duhaiby, Omar ; Mooij, Arjan ; van Wezep, Hans ; Groote, Jan Friso. / Pitfalls in applying model learning to industrial legacy software. Leveraging Applications of Formal Methods, Verification and Validation. Industrial Practice - 8th International Symposium, ISoLA 2018, Proceedings. redacteur / Tiziana Margaria ; Bernhard Steffen. Cham : Springer, 2018. blz. 121-138 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
    @inproceedings{6937f53e4a024f599994e59c6ca563eb,
    title = "Pitfalls in applying model learning to industrial legacy software",
    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.",
    keywords = "Active learning, Legacy software, Model learning",
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    al Duhaiby, O, Mooij, A, van Wezep, H & Groote, JF 2018, Pitfalls in applying model learning to industrial legacy software. in T Margaria & B Steffen (redactie), Leveraging Applications of Formal Methods, Verification and Validation. Industrial Practice - 8th International Symposium, ISoLA 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11247 LNCS, Springer, Cham, blz. 121-138, Limassol, Cyprus, 5/11/18. DOI: 10.1007/978-3-030-03427-6_13

    Pitfalls in applying model learning to industrial legacy software. / al Duhaiby, Omar; Mooij, Arjan; van Wezep, Hans; Groote, Jan Friso.

    Leveraging Applications of Formal Methods, Verification and Validation. Industrial Practice - 8th International Symposium, ISoLA 2018, Proceedings. redactie / Tiziana Margaria; Bernhard Steffen. Cham : Springer, 2018. blz. 121-138 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11247 LNCS).

    Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

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    AB - 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.

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    al Duhaiby O, Mooij A, van Wezep H, Groote JF. Pitfalls in applying model learning to industrial legacy software. In Margaria T, Steffen B, redacteurs, Leveraging Applications of Formal Methods, Verification and Validation. Industrial Practice - 8th International Symposium, ISoLA 2018, Proceedings. Cham: Springer. 2018. blz. 121-138. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Beschikbaar vanaf, DOI: 10.1007/978-3-030-03427-6_13