Improving Model Inference in Industry by Combining Active and Passive Learning
conference paper
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.
Topics
TNO Identifier
865623
ISBN
978-1-7281-0591-8
Publisher
IEEE
Source title
26th IEEE International Conference on Software Analysis, Evolution, and Reengineering (SANER 2019) - Hangzhou, China, 24 Feb 2019 - 27 Feb 2019
Collation
11 p.
Pages
253-263
Files
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