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2 results on '"runtime logs"'

1. Improving model inference in industry by combining active and passive learning

Author

Yang, Nan, Aslam, K., Schiffelers, R.R.H., Lensink, Leonard, Hendriks, D., Cleophas, L.G.W.A., Serebrenik, A., Shihab, Emad, Lo, David, Wang, Xinyu, Interconnected Resource-aware Intelligent Systems, and Software Engineering and Technology

Subjects
Reverse engineering, equivalence oracle, Active learning, Informatics, Model inference, Computer science, Active learning (machine learning), 02 engineering and technology, Machine learning, computer.software_genre, Passive learning, 020204 information systems, active learning, 0202 electrical engineering, electronic engineering, information engineering, Software system, Runtime logs, Finite-state machine, Industrial Innovation, business.industry, Equivalence oracle, 020207 software engineering, model inference, passive learning, reverse engineering, runtime logs, Artificial intelligence, Completeness (statistics), business, Conformance testing, computer
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.

Published
2019

2. Improving Model Inference in Industry by Combining Active and Passive Learning

Subjects
Runtime logs, Informatics, Active learning, Industrial Innovation, Equivalence oracle, Reverse engineering, Model inference, Passive learning
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.

Published
2019

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