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Increased realism in software engineering experiments is often promoted as an important means of increasing generalizability and industrial relevance. In this context, artificiality, e.g., the use of constructed tasks in place of realistic tasks, is seen as a threat. In this paper, we examine the opposite view that deliberately introduced artificial design elements may increase knowledge gain and enhance both generalizability and relevance. In the first part of this paper, we identify and evaluate arguments and examples in favor of and against deliberately introducing artificiality into software engineering experiments. We find that there are good arguments in favor of deliberately introducing artificial design elements to 1) isolate basic mechanisms, 2) establish the existence of phenomena, 3) enable generalization from particularly unfavorable to more favorable conditions (persistence of phenomena), and 4) relate experiments to theory. In the second part of this paper, we summarize a content analysis of articles that report software engineering experiments published over a 10-year period from 1993 to 2002. The analysis reveals a striving for realism and external validity, but little awareness of for what and when various degrees of artificiality and realism are appropriate. Furthermore, much of the focus on realism seems to be based on a narrow understanding of the nature of generalization. We conclude that an increased awareness and deliberation as to when and for what purposes both artificial and realistic design elements are applied is valuable for better knowledge gain and quality in empirical software engineering experiments. We also conclude that time spent on studies that have obvious threats to validity that are due to artificiality might be better spent on studies that investigate research questions for which artificiality is a strength rather than a weakness. However, arguments in favor of artificial design elements should not be used to justify studies that are badly designed or that have research questions of low relevance. [ABSTRACT FROM AUTHOR]

Empirically based theories are generally perceived as foundational to science. However, in many disciplines, the nature, role and even the necessity of theories remain matters for debate, particularly in young or practical disciplines such as software engineering. This article reports a systematic review of the explicit use of theory in a comprehensive set of 103 articles reporting experiments, from of a total of 5,453 articles published in major software engineering journals and conferences in the decade 1993-2002. Of the 103 articles, 24 use a total of 40 theories in various ways to explain the cause-effect relationship(s) under investigation. The majority of these use theory in the experimental design to justify research questions and hypotheses, some use theory to provide post hoc explanations of their results, and a few test or modify theory. A third of the theories are proposed by authors of the reviewed articles. The interdisciplinary nature of the theories used is greater than that of research in software engineering in general. We found that theory use and awareness of theoretical issues are present, but that theory-driven research is, as yet, not a major issue in empirical software engineering. Several articles comment explicitly on the lack of relevant theory. We call for an increased awareness of the potential benefits of involving theory, when feasible. To support software engineering researchers who wish to use theory, we show which of the reviewed articles on which topics use which theories for what purposes, as well as details of the theories' characteristics. [ABSTRACT FROM AUTHOR]