Abstract Model transformations are central to model-driven software development. Applications of model transformations include creating models, handling model co-evolution, model merging, and understanding model evolution. In the past, various (semi-)automatic approaches to derive model transformations from meta-models or from examples have been proposed. These approaches require time-consuming handcrafting or the recording of concrete examples, or they are unable to derive complex transformations. We propose a novel unsupervised approach, called Ockham , which is able to learn edit operations from model histories in model repositories. Ockham is based on the idea that meaningful domain-specific edit operations are the ones that compress the model differences. It employs frequent subgraph mining to discover frequent structures in model difference graphs. We evaluate our approach in two controlled experiments and one real-world case study of a large-scale industrial model-driven architecture project in the railway domain. We found that our approach is able to discover frequent edit operations that have actually been applied before. Furthermore, Ockham is able to extract edit operations that are meaningful—in the sense of explaining model differences through the edit operations they comprise—to practitioners in an industrial setting. We also discuss use cases (i.e., semantic lifting of model differences and change profiles) for the discovered edit operations in this industrial setting. We find that the edit operations discovered by Ockham can be used to better understand and simulate the evolution of models.

Many industrial software product lines use a clone-and-own approach for reuse among software products. As a result, the different products in the product line may drift apart, which implies increased efforts for tasks such as change propagation, domain analysis, and quality assurance. While many solutions have been proposed in the literature, these are often difficult to apply in a real-world setting. We study this drift of products in a concrete large-scale industrial model-driven clone-and-own software product line in the railway domain at our industry partner. For this purpose, we conducted interviews and a survey, and we investigated the models in the model history of this project. We found that increased efforts are mainly caused by large model differences and increased communication efforts. We argue that, in the short-term, treating the symptoms (i.e., handling large model differences) can help to keep efforts for software product-line engineering acceptable — instead of employing sophisticated variability management. To treat the symptoms, we employ a solution based on semantic-lifting to simplify model differences. Using the interviews and the survey, we evaluate the feasibility of variability management approaches and the semantic-lifting approach in the context of this project.

Model transformations play a fundamental role in model-driven software development. They can be used to solve or support central tasks, such as creating models, handling model co-evolution, and model merging. In the past, various (semi-)automatic approaches have been proposed to derive model transformations from meta-models or from examples. These approaches require time-consuming handcrafting or the recording of concrete examples, or they are unable to derive complex transformations. We propose a novel unsupervised approach, called Ockham, which is able to learn edit operations from model histories in model repositories. Ockham is based on the idea that meaningful domain-specific edit operations are the ones that compress the model differences. It employs frequent subgraph mining to discover frequent structures in model difference graphs. We evaluate our approach in two controlled experiments and one real-world case study of a large-scale industrial model-driven architecture project in the railway domain. We found that our approach is able to discover frequent edit operations that have actually been applied before. Furthermore, Ockham is able to extract edit operations that are meaningful to practitioners in an industrial setting.

Literature review studies are essential and form the foundation for any type of research. They serve as the point of departure for those seeking to understand a research topic, as well as, helps research communities to reflect on the ideas, fundamentals, and approaches that have emerged, been acknowledged, and formed the state-of-the-art. In this paper, we present a semi-systematic literature review of 218 papers published over the last four decades that have contributed to a better understanding of architectural design decisions (ADDs). These publications cover various related topics including tool support for managing ADDs, human aspects in architectural decision making (ADM), and group decision making. The results of this paper should be treated as a getting-started guide for researchers who are entering the investigation phase of research on ADM. In this paper, the readers will find a brief description of the contributions made by the established research community over the years. Based on those insights, we recommend our readers to explore the publications and the topics in depth.

The following topics are dealt with: learning (artificial intelligence); pattern classification; natural language processing; convolutional neural nets; feature extraction; neural nets; support vector machines; multi-agent systems; text analysis; vectors.

In large software engineering projects, designing software systems is a collaborative decision-making process where a group of architects and developers make design decisions on how to address design concerns by discussing alternative design solutions. For the decision-making process, involving appropriate individuals requires objectivity and awareness about their expertise. In this paper, we propose a novel expert recommendation system that identifies individuals who could be involved in tackling new design concerns in software engineering projects. The approach behind the proposed system addresses challenges such as identifying architectural skills, quantifying architectural expertise of architects and developers, and finally matching and recommending individuals with suitable expertise to discuss new design concerns. To validate our approach, a quantitative evaluation of the recommendation system was performed using design decisions from four software engineering projects. The evaluation not only indicates that individuals with architectural expertise can be identified for design decision making but also provides quantitative evidence for the existence of personal experience bias during the decision-making process.