We present a novel language-agnostic approach for integrating and preserving hand-written code in files generated via model-to-text transformation. Unlike existing approaches that only support modification of generated files in predefined locations (e.g., protected regions), the proposed approach allows users to make edits anywhere within generated files. Our hand-written code preservation technique has been implemented on top of an existing model-to-text transformation language (Epsilon's EGL). The approach is illustrated in a case study involving a code generator of Sirius Web editors and is contrasted with existing approaches such as protected regions and separation of generated and non-generated code using inheritance and delegation.

Serverless computing has shown vast potential for big data analytics applications, especially involving machine learning algorithms. Nevertheless, little consideration has been given in the literature to cloud-agnostic serverless architectures that leverage existing parallel implementations of machine learning algorithms. This work bridges this gap by proposing a multicloud serverless architecture for distributed machine learning, that enables machine learning engineers without cloud computing expertise to effortlessly port already implemented parallel machine learning algorithms to serverless, whilst overcoming vendor lock-in. In this work, two stateful machine learning algorithms have been ported to serverless, k-means clustering and logistic regression. The serverless implementation of k-means provided superior performance and scalability compared to a serverful implementation when using a number of workers that is equal to or slightly lower than the total number of vCPUs available on the VM running the serverful implementation. Additionally, it achieved an 87-fold speedup compared to a sequential implementation. Moreover, two storage designs of the shared state will be proposed for the serverless implementations, one that requires locks for updating the shared state, and another that is lock-free. Our experimental evaluation demonstrates that the performance of the lock-free serverless implementation of k-means declines with the increase in the number of clusters.

Tree-based model editors and hybrid graphical-textual model editors have advantages and limitations when editing domain models. Data is displayed hierarchically in tree-based model editors, whereas hybrid graphical-textual model editors capture high-level domain concepts graphically and low-level domain details textually. We conducted an empirical user study with 22 participants to evaluate the implicit assumption of system modellers that hybrid notations are superior, and to investigate the tradeoffs between the default EMF-based tree model editor and a Sirius/Xtext-based hybrid model editor. The results of the user study indicate that users largely prefer the hybrid editor and are more confident with hybrid notations for understanding the meaning of conditions. Furthermore, we found that the tree editor provided superior performance for analysing ordered lists of model elements, whereas activities requiring the comprehension or modelling of complex conditions were carried out faster through the hybrid editor.