Scientific publications

This report presents an overview of the work done by TNO-ESI and ASML in the Matala 2023 project. The main objective of the Matala project is to show how model-based testing can be used to improve efficiency and effectiveness of testing system-of-systems (SoS) in industry. The focus of testing is to ensure that systems are syntactically and semantically interoperable with each other, and deliver on promised functionality in an operational context.

Manually writing test cases takes a lot of time in software development projects. Modelbased testing is a promising approach to automate most of this work by generating tests from models. This, however, shifts the problem to the creation of these models. In the research described here, we investigate the use of existing ComMA models which already have been constructed to describe and analyse software interfaces and component constraints.

Today’s complexity of high-tech systems makes diagnosing system failures a tough task for service engineers. Increasing product variability and fast market introduction of new generation systems prohibit the expertise build-up that served service engineers in the past. Traditionally, system knowledge is transferred to the service organization through service manuals and training.

In this document, the specifications, commonalities, and fundamental differences between industrial use cases were gathered, analysed, and reported. Electron Microscopy (EM) and Unmanned Utility Vehicles (UUV) are the two target industrial use cases, which comprise sub use cases, are detailed down and described.

Model-based testing (MBT) promises a scalable solution to testing large systems, if a model is available. Creating these models for large systems, however, has proven to be difficult. Composing larger modelsfrom smaller ones could solve this, but our current MBT conformance relation uioco is not compositional, i.

We present our vision for creating an industrial legacy software rejuvenation toolchain. The goal is to semi automatically remove code smells from stateful software used in Cyber Physical Systems (CPS). Compared to existing tools that remove code smells, our toolchain can remove more than one type of code smell.

This deliverable describes two pilots performed by TNO-ESI together with Cordis and Additive Industries, for enhancing computer-aided design for low-code development, by applying synthesis. Specifying low-code (Cordis SUITE) models in a way to guarantee necessary safety/user requirements is difficult.

We present a method to identify the parameters of a state space model for a maxplus linear system based on the data from input-output sequences. This method is based on modeling the system as a mixed-integer program. We show that this method is computationally more efficient compared to existing methods, given the assumption that the system and data are not corrupted by noise.