Sebis Research News

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.

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.

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 

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.

The next SocioCortex Workshop will take place in conjunction with the next Sebis workshop on Thursday, 21st March 2017: Sebis Workshop