Performance Analysis of Model-Based Functional Identification on Modified Measuring Instruments

Modern measuring instruments are increasingly de-fined by complex software and simple hardware sensors. Currently established mechanisms to identify such systems, however, involve manual labor to verify version numbers and hashes over executable code, which is time-consuming. By ensuring identical f...

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Bibliographic Details
Main Authors: Xian Ho, Levin Chee, Esche, Marko, Nischwitz, Martin, Glesner, Sabine
Format: Conference Proceeding
Language:English
Subjects:
active automata learning
Continuous quality control
Current measurement
finite state machine
Instruments
Learning automata
Legal Metrology
model-based testing
Performance evaluation
Prototypes
Quality control
Software
software confor-mity analysis
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Summary:Modern measuring instruments are increasingly de-fined by complex software and simple hardware sensors. Currently established mechanisms to identify such systems, however, involve manual labor to verify version numbers and hashes over executable code, which is time-consuming. By ensuring identical functional behavior between a certified prototype and devices in the field, it is possible to realize remote automatic quality control of measuring instruments, even if version numbers or hashes differ. As such, the manufacturers of instruments could introduce minor patches or bugfixes without the need of reidentifying and recertifying the complete updated devices. In this paper, we present an approach that enables quality control of updated measuring instruments based on the L_{M}^{*} algorithm. With the algorithm, we infer a model that describes the functional behavior of an instrument by means of active automata learning. We analyze the performance of our approach with different test cases. It is envisioned that with our approach, we can pave the way towards realizing fully automatic quality control for software-driven measuring instruments.
ISSN:2642-2077
DOI:10.1109/I2MTC60896.2024.10561202