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.

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.

Applications offered as Software-as-a-Service should pursue a multi-tenant architecture in order to be profitable. Multi-tenancy helps to reduce the number of application instances by sharing resources amongst several tenants, thus saving operational costs, particularly in public cloud environments. While research mostly discusses how to design green-field software in a multi-tenant manner, this paper focuses on moving existing brown-field software into the Cloud thereby adding multi-tenancy. A low-effort approach is presented to leave the legacy application's source code unchanged. Only a couple of additional components have to be added taking care of multi-tenancy. Applying the aspect-oriented language AspectJ, we evaluate in a case study that such an approach is feasible: Tenant management, tenant-specific authentication, data isolation among multiple tenants for various database servers and strategies, and tenant-specific customization by modifying existing behavior can be achieved. These multi-tenancy features are provided in a reusable and modular framework that helps to apply them to other applications with a few lines of codes.

Database sharding is a technique to handle large data volumes efficiently by spreading data over a large number of machines. Sharding techniques are not only integral parts of NoSQL products, but also relevant for relational database servers if applications prefer standard relational database technology and also have to scale out with massive data. Sharding of relational databases is especially useful in a public Cloud because of the pay-per-use model, which already includes licenses, and the fast provisioning of virtually unlimited servers. In this paper, we investigate relational database sharding thereby focussing in detail on one of the important aspects of Cloud computing: the economical aspects. We discuss the difficulties of cost savings and present some surprising findings on how to save costs for database sharding.

Aspect-orientation handles crosscutting concerns in an adequate manner. It also promises a high potential for reuse due to a higher level of modularization since crosscutting concerns can better be modularized in aspects. This paper confirms higher reuse by presenting an AspectJ-based reuse approach. A collection of reusable aspects, e.g., for issues in concurrent programming, is presented. Moreover, an adequate Eclipse-based infrastructure is proposed that allows programmers to use those aspects easily within Eclipse. In particular, manual actions for configuration, management of dependencies, and weaving are eliminated. Using an Eclipse plug-in, aspects can simply be selected from an aspect repository, a corresponding JAR is downloaded from the repository, included and woven into the project.

Data migration between relational and object-oriented database systems is useful whenever relational data is needed in new object-oriented environments. However, implementing migration programs, i.e. converting and loading legacy data into the new database system, is cumbersome and must be done for each pair of databases manually. This paper presents a flexible approach to support data exchange using a federation approach. Given any relational database, a migration program for filling an object-oriented database is generated. The other direction can be automated in the same way.

This paper deals with generating interfaces to files. The presented approach consists of two main parts: first, describing the file structure in an object-oriented manner, and second, manipulating the files by means of either SQL or ODMG 2.0 object manipulation and querying. The principle of the approach is centered around a powerful specification language. Using this language, the structure of file contents is specified. Given such a specification, a generator automatically installs a corresponding relational database. Moreover, an adapter is generated that parses files and transfers the unstructured data from files to the structured relational database. Afterwards, the data can be handled by means of SQL. In addition, a semantic enrichment process can be used to achieve an object-oriented view of the data and an ODMG 2.0 conforming access.

Nearly all query languages discussed recently for the Entity-Relationship (ER) model do not possess a formal semantics. Languages are often defined by means of examples only. The reason for this phenomenon is the essential gap between features of query languages and theoretical foundations like algebras and calculi. Known languages offer arithmetic capabilities and allow for aggregates, but algebras and calculi defined for ER models do not. This paper introduces an extended ER model concentrating nearly all concepts of known so-called semantic data models in a few syntactical constructs. Moreover, we provide our extended ER model with a formal mathematical semantics. On this basis a well-founded calculus is developed taking into account data operations on arbitrary user-defined data types and aggregate functions. We pay special attention to arithmetic operations, as well as multivalued terms allowing nested queries, in a uniform and consistent manner. We prove our calculus only allows the formulation of safe terms and queries yielding a finite result, and to be (at least) as expressive as the relational calculi.