Industrial cyber-physical systems generate vast amounts of semi-structured time-series data that require careful preprocessing before they can be effectively used for machine learning applications such as fault detection and identification. Raw sensor datasets are often corrupted or incomplete, making it challenging to develop reliable solutions without proper data preparation and validation. In this paper, we introduce CPSLint, a domain-specific language for data validation and sanitisation. We present the design, implementation and evaluation of CPSLint, demonstrating its ability to automatically detect and correct common data corruption patterns while enabling non-programming domain experts to effectively prepare their data for analysis. We report evaluation results on a representative dataset, tracking memory consumption and CPU-time for sanitisation activities. Our approach offers several advantages over traditional methods, including reduced manual effort, guaranteed consistency and broader applicability across time-series datasets and projects.

Cyber-Physical Systems (CPSs) are large and complex systems, often playing a crucial role in the organisations in which they are deployed. Malfunctions in these systems are a significant concern, as they can be both costly and create unsafe situations. Downtime resulting from malfunctions can be mitigated through the use of robust diagnostic systems. CPSs share many commonalities across different domains. This fact can be used to formulate a broad methodology for developing diagnostic systems. Model-based systems engineering (MBSE) techniques, such as metamodelling, megamodelling, model slicing, and modelling through domain-specific languages, can provide a solid foundation for such a methodology. This paper outlines a PhD research project that aims to define such a MBSE-based methodology for defining diagnostics of CPSs using the aforementioned techniques. The research has been laid out in terms of the research questions that are to be investigated, the expected contributions, and the proposed approach to reach these contributions.

Interoperability across software languages is an important practical topic. In this paper, we take a deep dive into investigating and tackling the challenges involved with achieving interoperability between C and BabyCobol. The latter is a domain-specific language condensing challenges found in compiling legacy languages — borrowing directly from COBOL's data philosophy. Crossover, a compiler designed specifically to showcase the interoperability, exposes details of connecting a COBOL-like language with PICTURE clauses and re-entrant procedures, to C with primitive types and struct composites. Crossover features a C library for overcoming the differences between the data representations native to the respective languages. We illustrate the design process of Crossover and demonstrate its usage to provide a strategy to achieve interoperability between legacy and modern languages. The described process is aimed to be a blueprint for achievable interoperability between full-fledged COBOL and modern C-like programming languages.