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Paper titled Investigating the Role of Architects in Scaled 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 scaled 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 scaled agile frameworks, literature documenting the deployment of architect roles in scaled agile frameworks is still scarce.

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


Paper on Process and Tool-support to Collaboratively Formalize Statutory Texts by Executable Models by Bernhard Waltl, Thomas Reschenhofer, and Florian Matthes published on the DEXA Conference 2017

The interpretation of normative texts, such as laws, or con- tracts, is a complex, knowledge-, time-, and mostly data-intensive task. Numerous attempts have already been made to formalize this process, which have met rather less approval in legal science and practice.


This paper describes a collaborative modeling environment to support the analysis and interpretation of statutory texts, i.e., laws. The paper performs a case study on formalizing the product liability act and pro- poses a data-centric process that enables the formalization of laws. The tool implements state-of-the-art text mining technologies to assist legal experts during the formalization of norms by automatically detecting key concepts within normative texts, e.g., legal definitions, prohibitions, obligations, etc. The work at hand elaborates on the implementation of data science environment and describes key requirements, a reference architecture and a collaborative user interface.

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Paper on Web-based software-support for collaborative morphological analysis in real-time by Marin Zec and Florian Matthes published in the Journal Technological Forecasting and Social Change

Existing software and procedures for General Morphological Analysis (GMA) are primarily designed for synchronous face-to-face meetings. However, virtual teams and telework are on the rise. Against this background, we analyze current approaches and IT support to identify aspects that need to be reconsidered when GMA is applied in a distributed setting. In cooperation with a German non-profit cultural organization, we have developed browser-based collaborative GMA software that provides multi-user support. This paper presents what we have learned from the development process and the results from two empirical studies on the usability and learnability of the developed software. Based on observations and user feedback from the empirical studies, we conclude that the developed software is a useful IT artefact; more research is needed, however, to investigate the implications of distributed team settings for the application and facilitation of GMA.

see http://www.sciencedirect.com/science/article/pii/S0040162517306637 

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Paper on Ontology-based Approach for Software Architecture Recommendations accepted to the 23rd Americas Conference on Information Systems (AMCIS 2017)

Abstract

The design and development of sustainable software systems require software architects to consider a variety of architectural solutions and their trade-offs. With the frequent introduction of new architectural methods and software solutions, as well as, due to time-to-market constraints faced by software architects, considering even a subset of alternative architectural solutions during the decision-making process is a challenge. In this paper, we propose a recommendation system that automatically annotates architectural elements in software architecture documents and then proposes a) alternative architectural solutions for the annotated elements and b) concrete software solutions to realize an architectural design decision. These annotations and recommendations are derived from the knowledge captured in a publicly available cross-domain ontology. The evaluation of the recommendation system indicates that our approach can effectively support software architects to consider alternative architectural solutions while making architectural design decisions.