Scientific publications


Explore the publications from TNO‑ESI, showcasing our research findings and expertise. This includes peer-reviewed articles, conference papers, and research reports, as well as more accessible publications that share insights from our collaborations with industry partners. You can easily search the publications by keyword to find what is most relevant to you.

Year
2022
Authors
Dams, D.; Havelund, K.; Kauffman, S.

A Python Library for Trace Analysis

We present a Python library for trace analysis named PyContract. PyContract is a shallow internal DSL, in contrast to many trace analysis tools that implement external or deep internal DSLs. The library has been used in a project for analysis of logs from NASA’s Europa Clipper mission. We describe our design choices, explain the API via examples, and present an experiment comparing PyContract against other state-of-the-art tools from the research and industrial communities.
Year
2022
Authors
Wesselius, J.H.; Aker, J. van den; Doornbos, R.; Hendriks, T.; Marincic, J.; Tabingh Suermondt, W.

MBSE in the High-Tech Equipment Industry - MBSE-Study of ESI and Partners - Observations and Conclusions

Year
2022
Authors
Pil, A.

AI traint zich wedstrijdklaar op digital twin

Published in
Mechatronica & Machinebouw, 3, pp. 16-18.
Year
2022
Authors
Roos, N.

Een vruchtbare voedingsbodem voor systeemarchitectuur

Published in
Mechatronica & Machinebouw, 2, pp. 22-25.
Year
2022
Authors
Arocho, C.

Streamlining troubleshooting in the field

Published in
Bits & Chips(5), pp. 42-44.
Year
2022
Authors
Roos, N.

Sid and Ally provide AI assistance in bug resolution

Published in
Bits & Chips(4), pp. 26-28.
Year
2022
Authors
Roos, N.

Clearing the critical software path

Published in
Bits & Chips(3), pp. 26-28.
Year
2022
Authors
Roos, N.

Fertilizing the grounds for system architecting

Published in
Bits & Chips(2), pp. 20-24.
Year
2022
Authors
Hooimeijer, B.; Geilen, M.; Groote, J.; Hendriks, D.; Schiffelers, R.

Constructive Model Inference: Model Learning for Component-based Software Architectures

Model learning, learning a state machine from software, can be an effective model-based engineering technique, especially to understand legacy software. However, so far the applicability is limited as models that can be learned are quite small, often insufficient to represent the software behavior of large industrial systems.
Year
2022
Authors
Hilbrands, B.J.; Bera, D.; Akesson, B.

Partial Specifications of Component-based Systems using Petri Nets

Component-based architectures are commonly used in industry to manage the increasing complexity of systems. In such architectures, components interact with each other to achieve the desired functionality. They do so by providing and consuming services to and from each other over their defined interfaces.