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.
@cite_51 @cite_50 have also published a systematic review of design science articles in Information Systems. They identified articles by searching in top Information Systems journals and conferences from the years 2001--15, filtering the results and applying snow-balling, resulting in a final review sample of 119 papers or books. In their review, they analyze the topic addressed, artifact type, and evaluation method used. In our review we have classified papers along another dimension, i.e., what types of software engineering design science contributions the papers present in terms of problem understanding, solution design and solution validation. To our knowledge no reviews of software engineering literature have been made from a design science perspective before.
@cite_51 @cite_50
1686689482 2382843593 2092382400 2026750231
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 We present insights from a bibliometric analysis and scientific paper publication mining of 551 papers in Requirements Engineering (RE) series of conference (11 years from 2005 to 2015). We study cross-disciplinary and interdisciplinary nature of RE re- search by analyzing the cited disciplines in the reference section of each paper. We apply topic modeling on a corpus consisting of 551 abstracts and extract topics as frequently co-occurring and connected terms. We use topic modeling to study the structure and composition of RE research and analyze popular topics in industry as well as research track. Co-authorship in papers is an indicator of collaboration and interaction between scientists as well as institutions and we analyze co-authorship data to in- vestigate university-industry collaboration, internal and external collaborations. We present results on the distribution of the num- ber of co-authors in each paper as well as distribution of authors across world regions. We present our analysis on the public or proprietary dataset as well as the domain of the dataset used in studies published in Requirements Engineering (RE) series of conferences. Abstract This paper claims that a new field of software engineering research and practice is emerging: search-based software engineering. The paper argues that software engineering is ideal for the application of metaheuristic search techniques, such as genetic algorithms, simulated annealing and tabu search. Such search-based techniques could provide solutions to the difficult problems of balancing competing (and some times inconsistent) constraints and may suggest ways of finding acceptable solutions in situations where perfect solutions are either theoretically impossible or practically infeasible. In order to develop the field of search-based software engineering, a reformulation of classic software engineering problems as search problems is required. The paper briefly sets out key ingredients for successful reformulation and evaluation criteria for search-based software engineering. Reviews studies from 1991-2013 to assess application of ML techniques for SFP.Identifies seven categories of the ML techniques.Identifies 64 studies to answer the established research questions.Selects primary studies according to the quality assessment of the studies.Systematic literature review performs the following:Summarize ML techniques for SFP models.Assess performance accuracy and capability of ML techniques for constructing SFP models.Provide comparison between the ML and statistical techniques.Provide comparison of performance accuracy of different ML techniques.Summarize the strength and weakness of the ML techniques.Provides future guidelines to software practitioners and researchers. BackgroundSoftware fault prediction is the process of developing models that can be used by the software practitioners in the early phases of software development life cycle for detecting faulty constructs such as modules or classes. There are various machine learning techniques used in the past for predicting faults. MethodIn this study we perform a systematic review of studies from January 1991 to October 2013 in the literature that use the machine learning techniques for software fault prediction. We assess the performance capability of the machine learning techniques in existing research for software fault prediction. We also compare the performance of the machine learning techniques with the statistical techniques and other machine learning techniques. Further the strengths and weaknesses of machine learning techniques are summarized. ResultsIn this paper we have identified 64 primary studies and seven categories of the machine learning techniques. The results prove the prediction capability of the machine learning techniques for classifying module class as fault prone or not fault prone. The models using the machine learning techniques for estimating software fault proneness outperform the traditional statistical models. ConclusionBased on the results obtained from the systematic review, we conclude that the machine learning techniques have the ability for predicting software fault proneness and can be used by software practitioners and researchers. However, the application of the machine learning techniques in software fault prediction is still limited and more number of studies should be carried out in order to obtain well formed and generalizable results. We provide future guidelines to practitioners and researchers based on the results obtained in this work.