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.
| Original language | English |
|---|---|
| Title of host publication | Leveraging Applications of Formal Methods, Verification and Validation. Industrial Practice - 8th International Symposium, ISoLA 2018, Proceedings |
| Editors | Tiziana Margaria, Bernhard Steffen |
| Place of Publication | Cham |
| Publisher | Springer |
| Pages | 121-138 |
| Number of pages | 18 |
| ISBN (Electronic) | 978-3-030-03427-6 |
| ISBN (Print) | 978-3-030-03426-9 |
| DOIs | |
| Publication status | Published - 30 Oct 2018 |
| Event | 8th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation, (ISoLA 2018) - Limassol, Cyprus Duration: 5 Nov 2018 → 9 Nov 2018 http://www.isola-conference.org/isola2018/ |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 11247 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Conference
| Conference | 8th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation, (ISoLA 2018) |
|---|---|
| Abbreviated title | ISoLA2018 |
| Country | Cyprus |
| City | Limassol |
| Period | 5/11/18 → 9/11/18 |
| Internet address |
Fingerprint
Keywords
- Active learning
- Legacy software
- Model learning
Cite this
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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. ed. / Tiziana Margaria; Bernhard Steffen. Cham : Springer, 2018. p. 121-138 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11247 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - Pitfalls in applying model learning to industrial legacy software
AU - al Duhaiby, Omar
AU - Mooij, Arjan
AU - van Wezep, Hans
AU - Groote, Jan Friso
PY - 2018/10/30
Y1 - 2018/10/30
N2 - 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.
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.
KW - Active learning
KW - Legacy software
KW - Model learning
UR - http://www.scopus.com/inward/record.url?scp=85056469617&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-03427-6_13
DO - 10.1007/978-3-030-03427-6_13
M3 - Conference contribution
AN - SCOPUS:85056469617
SN - 978-3-030-03426-9
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 121
EP - 138
BT - Leveraging Applications of Formal Methods, Verification and Validation. Industrial Practice - 8th International Symposium, ISoLA 2018, Proceedings
A2 - Margaria, Tiziana
A2 - Steffen, Bernhard
PB - Springer
CY - Cham
ER -