Assessing composition in modeling approaches

Modeling approaches are based on various paradigms, e.g., aspect-oriented, feature-oriented, object-oriented, and logic-based. Modeling approaches may cover requirements models to low-level design models, are developed for various purposes, use various means of composition, and thus are difficult to compare. However, such comparisons are critical to help practitioners know under which conditions approaches are most applicable, and how they might be successfully generalized and combined to achieve end-to-end methods. This paper reports on work done at the 2nd International Comparing Modeling Approaches (CMA) workshop towards the goal of identifying potential comprehensive modeling methodologies with a particular emphasis on composition: (i) an improved set of comparison criteria; (ii) 19 assessments of modeling approaches based on the comparison criteria and a common, focused case study

Automotive Perception Software Development : An Empirical Investigation into Data, Annotation, and Ecosystem Challenges

Software that contains machine learning algorithms is an integral part of automotive perception, for example, in driving automation systems. The development of such software, specifically the training and validation of the machine learning components, requires large annotated datasets. An industry of data and annotation services has emerged to serve the development of such data-intensive automotive software components. Wide-spread difficulties to specify data and annotation needs challenge collaborations between OEMs (Original Equipment Manufacturers) and their suppliers of software components, data, and annotations.This paper investigates the reasons for these difficulties for practitioners in the Swedish automotive industry to arrive at clear specifications for data and annotations. The results from an interview study show that a lack of effective metrics for data quality aspects, ambiguities in the way of working, unclear definitions of annotation quality, and deficits in the business ecosystems are causes for the difficulty in deriving the specifications. We provide a list of recommendations that can mitigate challenges when deriving specifications and we propose future research opportunities to overcome these challenges. Our work contributes towards the on-going research on accountability of machine learning as applied to complex software systems, especially for high-stake applications such as automated driving. This project has received funding from Vinnova Swedenunder the FFI program with grant agreement No 2021-02572(precog), from the EU’s Horizon 2020 research and innovationprogram under grant agreement No 957197 (vedliot), and froma Swedish Research Council (VR) Project: Non-FunctionalRequirements for Machine Learning: Facilitating ContinuousQuality Awareness (iNFoRM).</p

An Agile and Ontology-Aided Modeling Environment

Part 5: Semantics and ReasoningInternational audienceEnterprise knowledge is currently subject to ever-changing, complex and domain-specific modeling requirements. Assimilating these requirements in modeling languages brings the benefits associated to both domain-specific modeling languages (DSMLs) and a baseline of well-established concepts. However, there are two problems that hamper the speed and efficiency of this activity: (1) the separation between the two key expertise: language engineering and domain knowledge, and (2) the sequential modeling language engineering life-cycles. In this work, we tackle these two challenges by introducing an Agile and Ontology-Aided approach implemented in our Modeling Environment - the AOAME. The approach seamlessly integrates meta-modeling and modeling in the same modeling environment, thus cooperation between language engineers and domain experts is fostered. Sequential engineering phases are avoided as the adaptation of the language is done on-the-fly. To this end, a modeling language is grounded with an ontology language providing a clear, unambiguous and machine-interpretable semantics. Mechanisms implemented in the AOAME ensure the propagation of changes from the modeling environment to the graph-based database containing the ontology