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Teilnahme des sebis Lehrstuhls am Forschungskolloquium Modellierung 2017 an der FernUniversität in Hagen

Auch dieses Jahr fand das Forschungskolloquium Modellierung in Kooperation mit teilnehmenden Lehrstühlen aus den Hochschulen Universität Bayreuth, Universität Duisburg-Essen, FernUniversität in Hagen, HTWG Konstanz und TU München statt. Durch spannende Vorträge wurden zahlreiche Themen aus den Bereichen Enterprise Modelling und Enterprise Architecture Management seitens der teilnehmenden Lehrstühle vorgestellt und diskutiert.


Paper Evaluating Natural Language Understanding Services for Conversational Question Answering Systems by Daniel Braun, Adrian Hernandez-Mendez, Florian Matthes, and Manfred Langen published at SIGdial 2017

Conversational interfaces recently gained a lot of attention. One of the reasons for the current hype is the fact that chatbots (one particularly popular form of conversational interfaces) nowadays can be created without any programming knowledge, thanks to different toolkits and so-called Natural Language Understanding (NLU) services. While these NLU services are already widely used in both, industry and science, so far, they have not been analysed systematically. In this paper, we present a method to evaluate the classification performance of NLU services. Moreover, we present two new corpora, one consisting of annotated questions and one consisting of annotated questions with the corresponding answers. Based on these corpora, we conduct an evaluation of some of the most popular NLU services. Thereby we want to enable both, researchers and companies to make more educated decisions about which service they should use.

Doctoral Symposium paper titled 'Towards a framework for managing architectural design decisions' accepted at the ECSA 2017

Software architecture is considered as a set of architectural design decisions. The recent trends, both in research and industry, call for improved tool support for software architects and developers to manage architectural design decisions and its associated concepts. As part of our ongoing work, we propose a framework for managing architectural design decisions in large software-intensive projects. Each component within this framework addresses specific use cases including (a) extraction and classification of design decisions from issue management systems, (b) annotation of architectural elements, (c) recommendation of alternative decision options, (d) reasoning about decisions’ rationale, and (e) recommendation of experts for addressing design decisions. These components are planned to be iteratively realized and evaluated using the design science research approach. We believe that the realization of such a framework will allow an architectural knowledge management systems to integrate with the design, development, and maintenance phases to support stakeholders not only to document design decisions but also to learn from decisions made in the past projects.

Paper titled Investigating the Role of Architects in Scaling Agile Frameworks by Ömer Uludag, Martin Kleehaus, Xian Xu, and Florian Matthes accepted at EDOC 2017

Abstract: Over the past two decades, agile software development methods have been adopted by an increasing number of organizations to improve their software development processes. In contrast to traditional methods, agile methods place more emphasis on flexible processes than on detailed upfront plans and heavy documentations. Since agile methods have proved to be successful at the team level, large organizations are now aiming to scale agile methods to the enterprise level by adopting so-called scaling agile frameworks. Scaling agile methods at the enterprise level with some amount of architectural planning prevents excessive redesign efforts and functional redundancy in application architectures. An effective evolution of application architectures requires the right trade-off between emergent and intentional architectural design and a close collaboration between agile and architecture teams. Although there is a growing body of literature on scaling agile frameworks, literature documenting the deployment of architect roles in scaling agile frameworks is still scarce.

This study describes the roles of architects in scaling agile frameworks with the help of a structured literature review. We aim to provide a primary analysis of 20 identified scaling agile frameworks. Subsequently, we thoroughly describe three popular scaling agile frameworks: Scaled Agile Framework, Large Scale Scrum, and Disciplined Agile 2.0. After specifying the main concepts of scaling agile frameworks, we characterize roles of enterprise, software, solution, and information architects, as identified in four scaling agile frameworks. Finally, we provide a discussion of generalizable findings on the role of architects in scaling agile frameworks

Paper titled Automatic extraction of design decisions from issue management systems - a machine learning based approach accepted at the ECSA 2017

Abstract: The need to explicitly document design decisions has been emphasized both in research and in industry. To address design concerns, software architects and developers implicitly capture design decisions in tools such as issue management systems. These design decisions are not explicitly labeled and are not integrated with the architecture knowledge management tools. Automatically extracting these design decisions will aid the architectural knowledge management tools to learn from the past decisions and to guide architects while making decisions in similar context. In this paper, we propose a two-phase supervised machine learning based approach to, first, automatically detect design decisions from issues and second, to automatically classify the identified design decisions into different decision categories. We have manually analyzed and labeled more than 1,500 issues from two large open source repositories and have used this dataset for generating the machine learning models. We have made the dataset publicly available that will serve as a starting point for researchers to further reference and investigate the design decision detection and classification problem. Our evaluation shows that by using linear support vector machines, we can detect design decisions with 91.29% accuracy and classify them with an accuracy of 82.79%. This provides a quantitative basis for learning from past design decisions to support stakeholders in making better and informed design decisions.