A Model-Based Approach to Impact Analysis Using Model Differencing

Impact analysis is concerned with the identification of consequences of changes and is therefore an important activity for software evolution. In modelbased software development, models are core artifacts, which are often used to generate essential parts of a software system. Changes to a model can thus substantially affect different artifacts of a software system. In this paper, we propose a modelbased approach to impact analysis, in which explicit impact rules can be specified in a domain specific language (DSL). These impact rules define consequences of designated UML class diagram changes on software artifacts and the need of dependent activities such as data evolution. The UML class diagram changes are identified automatically using model differencing. The advantage of using explicit impact rules is that they enable the formalization of knowledge about a product. By explicitly defining this knowledge, it is possible to create a checklist with hints about development steps that are (potentially) necessary to manage the evolution. To validate the feasibility of our approach, we provide results of a case study.Comment: 16 pages, 5 figures, In: Proceedings of the 8th International Workshop on Software Quality and Maintainability (SQM), ECEASST Journal, vol. 65 201

Model Matching Challenge: Benchmarks for Ecore and BPMN Diagrams

In the last couple of years, Model Driven Engineering (MDE) gained a prominent role in the context of software engineering. In the MDE paradigm, models are considered first level artifacts which are iteratively developed by teams of programmers over a period of time. Because of this, dedicated tools for versioning and management of models are needed. A central functionality within this group of tools is model comparison and differencing. In two disjunct research projects, we identified a group of general matching problems where state-of-the-art comparison algorithms delivered low quality results. In this article, we will present five edit operations which are the cause for these low quality results. The reasons why the algorithms fail, as well as possible solutions, are also discussed. These examples can be used as benchmarks by model developers to assess the quality and applicability of a model comparison tool for a given model type.Comment: 7 pages, 7 figure

Methods for Interpreting and Understanding Deep Neural Networks

This paper provides an entry point to the problem of interpreting a deep neural network model and explaining its predictions. It is based on a tutorial given at ICASSP 2017. It introduces some recently proposed techniques of interpretation, along with theory, tricks and recommendations, to make most efficient use of these techniques on real data. It also discusses a number of practical applications.Comment: 14 pages, 10 figure

Strukturentwicklungen im Handwerk

Das Handwerk ist ein wichtiger Sektor der deutschen Volkswirtschaft. Die Erscheinungsformen handwerklicher Betriebe haben sich in den vergangenen Jahren aber entscheidend verändert. Während früher die mittleren Größenklassen dominierten, haben heute die Ränder an Bedeutung gewonnen. Dies sind zum einen Kleinst- und Ein-Personen-Unternehmen, deren Anteil stark gestiegen ist, und zum anderen handwerkliche Großbetriebe.The skilled crafts are still an essential element in the German economy. The characteristics of these companies, however, have changed dramatically in recent years. Previously, medium-scale enterprises of approximately 5 to 49 employees dominated. In recent years, however, a polarisation of the skilled crafts spectrum has occurred. Partially due to the amendment of the Trades and Crafts Code in 2004, the share of small and one-person businesses has risen sharply. Additionally, larger-scale enterprises play an increasingly important role, and this development is only partially reflected in the statistical data