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Personality tests in various guises are commonly used in recruitment and career counseling industries. Such tests have also been considered as instruments for predicting the job performance of software professionals both individually and in teams. However, research suggests that other human-related factors such as motivation, general mental ability, expertise, and task complexity also affect the performance in general. This paper reports on a study of the impact of the Big Five personality traits on the performance of pair programmers together with the impact of expertise and task complexity. The study involved 196 software professionals in three countries forming 98 pairs. The analysis consisted of a confirmatory part and an exploratory part. The results show that: 1) Our data do not confirm a meta-analysis-based model of the impact of certain personality traits on performance and 2) personality traits, in general, have modest predictive value on pair programming performance compared with expertise, task complexity, and country. We conclude that more effort should be spent on investigating other performance-related predictors such as expertise, and task complexity, as well as other promising predictors, such as programming skill and learning. We also conclude that effort should be spent on elaborating on the effects of personality on various measures of collaboration, which, in turn, may be used to predict and influence performance. Insights into such malleable, rather than static, factors may then be used to improve pair programming performance. [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]

The classical method for identifying cause-effect relationships is to conduct controlled experiments. This paper reports upon the present state of how controlled experiments in software engineering are conducted and the extent to which relevant information is reported. Among the 5,453 scientific articles published in 12 leading software engineering journals and conferences in the decade from 1993 to 2002, 103 articles (1.9 percent) reported controlled experiments in which individuals or teams performed one or more software engineering tasks. This survey quantitatively characterizes the topics of the experiments and their subjects (number of subjects, students versus professionals, recruitment, and rewards for participation), tasks (type of task, duration, and type and size of application) and environments (location, development tools). Furthermore, the survey reports on how internal and external validity is addressed and the extent to which experiments are replicated. The gathered data reflects the relevance of software engineering experiments to industrial practice and the scientific maturity of software engineering research. [ABSTRACT FROM AUTHOR]