Improving Model Inference in Industry by Combining Active and Passive Learning

Yang, Nan; Aslam, Kousar; Schiffelers, R.R.H.; Lensink, Leonard; Hendriks, Dennis; Cleophas, Loek; Serebrenik, Alexander

doi:10.1109/saner.2019.8668007

Cited by 19 publications

(17 citation statements)

References 26 publications

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“…Yang et al [67] have proposed to use software execution logs to refine the hypothesis proposed by the learner, before posting it to the conformance testing algorithm. The logbased oracle is built on the observation that logs represent real behavior of the system.…”

Section: Log-based Oraclementioning

confidence: 99%

“…We apply active learning on industrial MDE-based control software components from a sub-system of ASML's TWINSCAN machine [67]. Using ASD technology, the components were initially designed in 2012 .…”

Section: Study Objectmentioning

confidence: 99%

“…In this paper, we present the research that we have conducted to support an automated and cost-effective transition from existing software to models. We try to achieve this by developing techniques which use information available in source code, execution traces and/or other software documentation to infer interface protocols [5,67]. The interface protocols, once learned, can serve as the starting point for several engineering and maintenance activities, which is discussed in Sect.…”

mentioning

confidence: 99%

“…Using our framework, we applied active learning on 202 MDE-based software components from our industrial partner ASML's lithography machines software. Testing is known to be the bottleneck of active learning process [59,67]. To achieve best results, and benefiting from our automated framework, we applied active learning using several testing methods.…”

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confidence: 99%

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Interface protocol inference to aid understanding legacy software components

et al. 2020

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High-tech companies are struggling today with the maintenance of legacy software. Legacy software is vital to many organizations as it contains the important business logic. To facilitate maintenance of legacy software, a comprehensive understanding of the software’s behavior is essential. In terms of component-based software engineering, it is necessary to completely understand the behavior of components in relation to their interfaces, i.e., their interface protocols, and to preserve this behavior during the maintenance activities of the components. For this purpose, we present an approach to infer the interface protocols of software components from the behavioral models of those components, learned by a blackbox technique called active (automata) learning. To validate the learned results, we applied our approach to the software components developed with model-based engineering so that equivalence can be checked between the learned models and the reference models, ensuring the behavioral relations are preserved. Experimenting with components having reference models and performing equivalence checking builds confidence that applying active learning technique to reverse engineer legacy software components, for which no reference models are available, will also yield correct results. To apply our approach in practice, we present an automated framework for conducting active learning on a large set of components and deriving their interface protocols. Using the framework, we validated our methodology by applying active learning on 202 industrial software components, out of which, interface protocols could be successfully derived for 156 components within our given time bound of 1 h for each component.

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Section: Log-based Oraclementioning

confidence: 99%

Section: Study Objectmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Interface protocol inference to aid understanding legacy software components

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“…This tool suite is applied and validated in the industrial context of ASML. Recently, this tool suite has been applied to 218 control software components of ASML's TWINSCAN lithography machines [26]. 118 components could be learned in an hour or less.…”

Section: Industrial Tracksmentioning

confidence: 99%

RERS 2019: Combining Synthesis with Real-World Models

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et al. 2019

Tools and Algorithms for the Construction and Analysis of Systems

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This paper covers the Rigorous Examination of Reactive Systems (RERS) Challenge 2019. For the first time in the history of RERS, the challenge features industrial tracks where benchmark programs that participants need to analyze are synthesized from real-world models. These new tracks comprise LTL, CTL, and Reachability properties. In addition, we have further improved our benchmark generation infrastructure for parallel programs towards a full automation. RERS 2019 is part of TOOLympics, an event that hosts several popular challenges and competitions. In this paper, we highlight the newly added industrial tracks and our changes in response to the discussions at and results of the last RERS Challenge in Cyprus.

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