Rolls-Royce Control Systems supplies engine control and monitoring systems for aviation applications, and is required to design, certify, and deliver these with the highest level of safety assurance. To allow Rolls-Royce to develop these systems, which continue to increase in complexity, model-based techniques are now a critical part of the software development process. At MODELS 2021 we presented early experiences with using and maintaining a bespoke domain-specific modelling workbench based on open-source modelling technologies, including the Eclipse Modelling Framework (EMF), Xtext, Sirius, and Epsilon. In this paper, we build on our previous paper with further insights, new challenges and lessons learnt as we have advanced and matured our domain-specific solution. We also discuss our experiences with moving towards web based modelling tools based on open-source technologies including Sirius Web, Eclipse GLSP and Eclipse Theia. Rolls-Royce intends to use a selection of these technologies to build a web-based modelling workbench, which will be used to architect and integrate the software for future Rolls-Royce engine control and monitoring systems in a collaborative way.

Model comparison is a prerequisite task for several other model management tasks such as model merging, model differencing etc. We present a novel approach to efficiently compare models using programs written in a rule-based model comparison language. As the comparison is done at the model element level, and each element needs to be traversed and compared with its corresponding elements, the execution of these comparison algorithms can be computationally expensive for larger models. In this paper, we present an efficient comparison approach which provides an automated rewriting facility to compare (both homogeneous and heterogeneous) models, based on static program analysis. Using this analysis, we reduce the search space by pre-filtering/indexing model elements, before actually comparing them. Moreover, we reorder the comparison match rules according to the dependencies between these rules to reduce the cost of jumping between rules. Our experiments demonstrate that the proposed model comparison approach delivers significant performance benefits in terms of execution time compared to the default ECL execution engine.

Model-to-model (M2M) transformation is a key ingredient in a typical Model-Driven Engineering workflow and there are several tailored high-level interpreted languages for capturing and executing such transformations. While these languages enable the specification of concise transformations through task-specific constructs (rules/mappings, bindings), their use can pose scalability challenges when it comes to very large models. In this paper, we present an architecture for optimising the execution of model-to-model transformations written in such a language, by leveraging static analysis and automated program rewriting techniques. We demonstrate how static analysis and dependency information between rules can be used to reduce the size of the transformation trace and to optimise certain classes of transformations. Finally, we detail the performance benefits that can be delivered by this form of optimisation, through a series of benchmarks performed with an existing transformation language (Epsilon Transformation Language - ETL) and EMF-based models. Our experiments have shown considerable performance improvements compared to the existing ETL execution engine, without sacrificing any features of the language.

This paper presents a solution for incorporating the structural information along with local features to enhance the recognition accuracy of human-vehicle interaction activities. Proposed system aims to exploit Bag of Words for extracting structural information both spatial and temporal relationship between features from video data to help achieve better recognition accuracy for complex interaction scenes. Traditional Bag of Words (BOW) approach is inefficient in representing structural information, feature positions and their temporal relationships which makes it difficult for the classifier to recognise interaction and complex scenes. The classifier uses BOW along with spatial and temporal positions of features. Random Forest and kNN are used as classifiers to compare classification results and to find a trade-off between recognition accuracy and computational complexity. We have used state of the art dataset VIRAT (Video and Image Retrieval and Analysis Tool) for validation of our scheme. Random Forest and modified BOW (RF+mBOW) gives better recognition accuracy at the cost of higher computational time whereas kNN and modified BOW (kNN+mBOW) takes less time for computations while giving remarkable recognition results. We observed that Random Forest and modified BOW (RF+mBOW) outperforms all state of art methodologies.