Publications

Causal modelling refers to modelling cause-effect relationships in systems, such as to understand how one thing causes another. Causality is becoming an essential element in models that try to make sense of the world. They range in purpose from mainly facilitating communication to making precise predictions on the consequences of choices, and to evaluate the past retrospectively.

ComMA (an abbreviation of Component Modeling and Analysis) is an approach for modeling components and their interfaces in an implementation agnostic notation. The approach is supported by the open-source tool Eclipse CommaSuite. It supports, for instance, model validation and monitoring to check whether the execution of an implementation conforms to the specification.

When humans rely on interacting with AI enabled systems, notably to support a critical decision- making process, it is crucial that the human interactors understand the outcomes in an explainable way. While this is less important for non-critical applications, business, safety, and mission critical applications require this transparency when accountability and compliance to regulations is demanded.

High-tech original equipment manufacturers rely on long-living, software-intensive complex systems that must continuously evolve to meet fast-paced market demands. This evolution often leads to the accumulation of technical debt, making maintenance increasingly challenging. This legacy code problem highlights the need for efficient strategies to keep these systems adaptable and fit-for-purpose.

Modern Integrated Development Environments (IDEs) offer automated refactorings to aid programmers in developing and maintaining software. However, implementing sound automated refactorings is challenging, as refactorings may inadvertently introduce name-binding errors or cause references to resolve to incorrect declarations.

TNO-ESI was asked to help the Netherlands armed forces take their systems engineering know-how to the next level. After a successful pilot training with twenty participants, four additional groups of twenty will be upskilled this year.

In the Hybrid Analysis project, we investigated combining static and dynamic information in order to analyse large-scale industrial software. Our goal was to assess the value of adding dynamic data to the static analysis that is already being done on a routine basis and to discover best practices for performing such hybrid analysis.

High-mix low-volume (HMLV) production systems are crucial for enabling mass customization to meet diverse and rapidly changing customer demands. These systems require optimization across various time scales to handle fluctuations in job mix and ensure robustness against unforeseen changes, such as rush orders.

In applied research projects, we want to create impact by developing innovative solutions, and land them in the organization. Successfully landing innovations typically requires a direct collaboration with stakeholders in the organization. They need to adopt the results in the system they develop or embed them in their systems development process and way-ofworking.