Improving model inference in industry by combining active and passive learning

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

1 Citaat (Scopus)

Uittreksel

Inferring behavioral models (e.g., state machines) of software systems is an important element of re-engineering activities. Model inference techniques can be categorized as active or passive learning, constructing models by (dynamically) interacting with systems or (statically) analyzing traces, respectively. Application of those techniques in the industry is, however, hindered by the trade-off between learning time and completeness achieved (active learning) or by incomplete input logs (passive learning). We investigate the learning time/completeness achieved trade-off of active learning with a pilot study at ASML, provider of lithography systems for the semiconductor industry. To resolve the trade-off we advocate extending active learning with execution logs and passive learning results.We apply the extended approach to eighteen components used in ASML TWINSCAN lithography machines. Compared to traditional active learning, our approach significantly reduces the active learning time. Moreover, it is capable of learning the behavior missed by the traditional active learning approach.

Originele taal-2Engels
Titel26th IEEE International Conference on Software Analysis, Evolution, and Reengineering (SANER 2019)
RedacteurenEmad Shihab, David Lo, Xinyu Wang
Plaats van productiePiscataway
UitgeverijInstitute of Electrical and Electronics Engineers
Pagina's253-263
Aantal pagina's11
ISBN van elektronische versie9781728105918
DOI's
StatusGepubliceerd - 15 mrt 2019
Evenement26th IEEE International Conference on Software Analysis, Evolution, and Reengineering, SANER 2019 - Hangzhou, China
Duur: 24 feb 201927 feb 2019
Congresnummer: 26

Congres

Congres26th IEEE International Conference on Software Analysis, Evolution, and Reengineering, SANER 2019
Verkorte titelSANER
LandChina
StadHangzhou
Periode24/02/1927/02/19

Vingerafdruk

Industry
Lithography
Problem-Based Learning
Semiconductor materials

Citeer dit

Yang, N., Aslam, K., Schiffelers, R. R. H., Lensink, L., Hendriks, D., Cleophas, L. G. W. A., & Serebrenik, A. (2019). Improving model inference in industry by combining active and passive learning. In E. Shihab, D. Lo, & X. Wang (editors), 26th IEEE International Conference on Software Analysis, Evolution, and Reengineering (SANER 2019) (blz. 253-263). [8668007] Piscataway: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/SANER.2019.8668007
Yang, Nan ; Aslam, K. ; Schiffelers, R.R.H. ; Lensink, Leonard ; Hendriks, D. ; Cleophas, L.G.W.A. ; Serebrenik, A. / Improving model inference in industry by combining active and passive learning. 26th IEEE International Conference on Software Analysis, Evolution, and Reengineering (SANER 2019). redacteur / Emad Shihab ; David Lo ; Xinyu Wang. Piscataway : Institute of Electrical and Electronics Engineers, 2019. blz. 253-263
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abstract = "Inferring behavioral models (e.g., state machines) of software systems is an important element of re-engineering activities. Model inference techniques can be categorized as active or passive learning, constructing models by (dynamically) interacting with systems or (statically) analyzing traces, respectively. Application of those techniques in the industry is, however, hindered by the trade-off between learning time and completeness achieved (active learning) or by incomplete input logs (passive learning). We investigate the learning time/completeness achieved trade-off of active learning with a pilot study at ASML, provider of lithography systems for the semiconductor industry. To resolve the trade-off we advocate extending active learning with execution logs and passive learning results.We apply the extended approach to eighteen components used in ASML TWINSCAN lithography machines. Compared to traditional active learning, our approach significantly reduces the active learning time. Moreover, it is capable of learning the behavior missed by the traditional active learning approach.",
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Yang, N, Aslam, K, Schiffelers, RRH, Lensink, L, Hendriks, D, Cleophas, LGWA & Serebrenik, A 2019, Improving model inference in industry by combining active and passive learning. in E Shihab, D Lo & X Wang (redactie), 26th IEEE International Conference on Software Analysis, Evolution, and Reengineering (SANER 2019)., 8668007, Institute of Electrical and Electronics Engineers, Piscataway, blz. 253-263, 26th IEEE International Conference on Software Analysis, Evolution, and Reengineering, SANER 2019, Hangzhou, China, 24/02/19. https://doi.org/10.1109/SANER.2019.8668007

Improving model inference in industry by combining active and passive learning. / Yang, Nan; Aslam, K.; Schiffelers, R.R.H.; Lensink, Leonard; Hendriks, D.; Cleophas, L.G.W.A.; Serebrenik, A.

26th IEEE International Conference on Software Analysis, Evolution, and Reengineering (SANER 2019). redactie / Emad Shihab; David Lo; Xinyu Wang. Piscataway : Institute of Electrical and Electronics Engineers, 2019. blz. 253-263 8668007.

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

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AU - Lensink, Leonard

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AU - Serebrenik, A.

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N2 - Inferring behavioral models (e.g., state machines) of software systems is an important element of re-engineering activities. Model inference techniques can be categorized as active or passive learning, constructing models by (dynamically) interacting with systems or (statically) analyzing traces, respectively. Application of those techniques in the industry is, however, hindered by the trade-off between learning time and completeness achieved (active learning) or by incomplete input logs (passive learning). We investigate the learning time/completeness achieved trade-off of active learning with a pilot study at ASML, provider of lithography systems for the semiconductor industry. To resolve the trade-off we advocate extending active learning with execution logs and passive learning results.We apply the extended approach to eighteen components used in ASML TWINSCAN lithography machines. Compared to traditional active learning, our approach significantly reduces the active learning time. Moreover, it is capable of learning the behavior missed by the traditional active learning approach.

AB - Inferring behavioral models (e.g., state machines) of software systems is an important element of re-engineering activities. Model inference techniques can be categorized as active or passive learning, constructing models by (dynamically) interacting with systems or (statically) analyzing traces, respectively. Application of those techniques in the industry is, however, hindered by the trade-off between learning time and completeness achieved (active learning) or by incomplete input logs (passive learning). We investigate the learning time/completeness achieved trade-off of active learning with a pilot study at ASML, provider of lithography systems for the semiconductor industry. To resolve the trade-off we advocate extending active learning with execution logs and passive learning results.We apply the extended approach to eighteen components used in ASML TWINSCAN lithography machines. Compared to traditional active learning, our approach significantly reduces the active learning time. Moreover, it is capable of learning the behavior missed by the traditional active learning approach.

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Yang N, Aslam K, Schiffelers RRH, Lensink L, Hendriks D, Cleophas LGWA et al. Improving model inference in industry by combining active and passive learning. In Shihab E, Lo D, Wang X, redacteurs, 26th IEEE International Conference on Software Analysis, Evolution, and Reengineering (SANER 2019). Piscataway: Institute of Electrical and Electronics Engineers. 2019. blz. 253-263. 8668007 https://doi.org/10.1109/SANER.2019.8668007