Background: Communicating software engineering research to industry practitioners and to other researchers can be challenging due to its context dependent nature. Design science is recognized as a pragmatic research paradigm, addressing this and other characteristics of applied and prescriptive research. Applying the design science lens to software engineering research may improve the communication of research contributions. Aim: The aim of this study is to 1) evaluate how well the design science lens helps frame software engineering research contributions, and 2) identify and characterize different types of design science contributions in the software engineering literature. Method: In previous research we developed a visual abstract template, summarizing the core constructs of the design science paradigm. In this study, we use this template in a review of a selected set of 38 top software engineering publications to extract and analyze their design science contributions. Results: We identified five clusters of papers, classified based on their alignment to the design science paradigm. Conclusions: The design science lens helps to pinpoint the theoretical contribution of a research output, which in turn is the core for assessing the practical relevance and novelty of the prescribed rule as well as the rigor of applied empirical methods in support of the rule.
In this paper, we do not distinguish between knowledge problems and solution problems within the design sciences but stress that the researcher's task is always to produce knowledge, which in turn can be used by practitioners for solving their problems. Such knowledge may be embedded in artefacts such as tools, models and techniques or distilled to simple technological rules. In line with van Aken @cite_9 , we distinguish between the explanatory sciences and the design sciences as two different paradigms producing different types of theory (explanatory and prescriptive respectively) with different validity criteria. This is similar to Stol and Fizgerald's distinction between knowledge seeking research and solution seeking research @cite_44 . In our study, we identified one cluster of software engineering papers belonging to the explanatory sciences ('descriptive') and three clusters of papers belonging to the design sciences ('problem-solution', 'solution-design' and 'solution evaluation').
@cite_44 @cite_9
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Design science emphasizes the connection between knowledge and practice by showing that we can produce scientific knowledge by designing useful things. However, without further guidelines, aspiring design science researchers tend to identify practical problems with knowledge questions, which may lead to methodologically unsound research designs. To solve a practical problem, the real world is changed to suit human purposes, but to solve a knowledge problem, we acquire knowledge about the world without necessarily changing it. In design science, these two kinds of problems are mutually nested, but this nesting should not blind us for the fact that their problem-solving and solution justification methods are different. This paper analyzes the mutual nesting of practical problems and knowledge problems, derives some methodological guidelines from this for design science researchers, and gives an example of a design science project following this problem nesting. The publication of the work on design science by Alan Hevner and his colleagues has fostered much discussion on what is and what is not considered to be design science in Information Systems (IS) research. Anecdotal evidence suggests that some authors claim design science as a methodology in their work, without much consideration of theoretical or methodological aspects, or the appropriateness of their artefact. Also it would appear that design science papers have been proliferating rapidly of late. Accordingly, we were interested to identify the proliferation, nature and quality of design science research in Information Systems conference publications since the publication of ’s work in 2004. We examine design science articles published at five major IS conferences over the last three years. We subject 83 articles, identified as relevant via a rigorous analysis process, to three types of analysis - statistical, thematic and methodological. The results of these analyses indicate that design science appears to be a growing stream of research in IS. We also found design science research to be strongly prevalent in the research domains of process, knowledge and information management. The most interesting results stem out of our methodological analysis, which suggests that only a small percentage of the papers discuss a concise and consistent implementation of the design science methodology suggested by Abstract Context For many years, we have observed industry struggling in defining a high quality requirements engineering (RE) and researchers trying to understand industrial expectations and problems. Although we are investigating the discipline with a plethora of empirical studies, they still do not allow for empirical generalisations. Objective To lay an empirical and externally valid foundation about the state of the practice in RE, we aim at a series of open and reproducible surveys that allow us to steer future research in a problem-driven manner. Method We designed a globally distributed family of surveys in joint collaborations with different researchers and completed the first run in Germany. The instrument is based on a theory in the form of a set of hypotheses inferred from our experiences and available studies. We test each hypothesis in our theory and identify further candidates to extend the theory by correlation and Grounded Theory analysis. Results In this article, we report on the design of the family of surveys, its underlying theory, and the full results obtained from Germany with participants from 58 companies. The results reveal, for example, a tendency to improve RE via internally defined qualitative methods rather than relying on normative approaches like CMMI. We also discovered various RE problems that are statistically significant in practice. For instance, we could corroborate communication flaws or moving targets as problems in practice. Our results are not yet fully representative but already give first insights into current practices and problems in RE, and they allow us to draw lessons learnt for future replications. Conclusion Our results obtained from this first run in Germany make us confident that the survey design and instrument are well-suited to be replicated and, thereby, to create a generalisable empirical basis of RE in practice. This book is an introductory text on design science, intended to support both graduate students and researchers in structuring, undertaking and presenting design science work. It builds on established design science methods as well as recent work on presenting design science studies and ethical principles for design science, and also offers novel instruments for visualizing the results, both in the form of process diagrams and through a canvas format. While the book does not presume any prior knowledge of design science, it provides readers with a thorough understanding of the subject and enables them to delve into much deeper detail, thanks to extensive sections on further reading. Design science in information systems and technology aims to create novel artifacts in the form of models, methods, and systems that support people in developing, using and maintaining IT solutions. This work focuses on design science as applied to information systems and technology, but it also includes examples from, and perspectives of, other fields of human practice. Chapter 1 provides an overview of design science and outlines its ties with empirical research. Chapter 2 discusses the various types and forms of knowledge that can be used and produced by design science research, while Chapter 3 presents a brief overview of common empirical research strategies and methods. Chapter 4 introduces a methodological framework for supporting researchers in doing design science research as well as in presenting their results. This framework includes five core activities, which are described in detail in Chapters 5 to 9. Chapter 10 discusses how to communicate design science results, while Chapter 11 compares the proposed methodological framework with methods for systems development and shows how they can be combined. Chapter 12 discusses how design science relates to research paradigms, in particular to positivism and interpretivism. Lastly, Chapter 13 discusses ethical issues and principles for design science research.