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Pattern discovery, the process of discovering previously unrecognized patterns, is usually performed as an ad-hoc process with little resulting certainty in the quality of the proposed patterns. Pattern validation, the process of validating the accuracy of proposed patterns, has rarely gone beyond the simple heuristic of "the rule of three". This article shows how to use established scientific research methods for the purpose of pattern discovery and validation. The result is an approach to pattern discovery and validation that can provide the same certainty that traditional scientific research methods can provide for the theories they are used to validate. This article describes our approach and explores its usefulness for pattern discovery and evaluation in a series of studies.
In qualitative research, results often emerge through an analysis process called coding. A common measure of validity of theories built through qualitative research is the agreement between different people coding the same materials. High intercoder agreement indicates that the findings are derived from the data as opposed to being relative results based on the original researcher's bias. However, measuring such intercoder agreement incurs the high cost of having additional researchers perform seemingly redundant work. In this paper we present first results on a novel method of using students for validating theories. We find that intercoder agreement between a large number of students is almost as good as the intercoder agreement between two professionals working on the same materials.
Free/libre and open source software are frequently described as a single community or movement. The difference between free software and open source ideology may influence founders, resulting in different types of companies being created. Specifically, the relationship between free/libre software ideology and social entrepreneurships is investigated. This paper presents seven case studies of businesses, five of which were founded by people who identify with the free/libre software movement. The result is a theory that small businesses founded by free/libre software advocates have three characteristics of social entrepreneurships. First, social benefit is prioritized over wealth creation. Second, the business’s social mission is not incidental but is furthered through its for-profit activities, rather than supported by the company’s profits. Third, the company’s success is defined in part by the success of its social mission Free/libre software entrepreneurs who recognize their activities as social entrepreneurships can benefit from the existing literature on the unique challenges faced by socially-oriented businesses.
We draw on the concept of episodic volunteering (EV) from the general volunteering literature to identify practices for managing EV in free/libre/open source software (FLOSS) communities. Infrequent but ongoing participation is widespread, but the practices that community managers are using to manage EV, and their concerns about EV, have not been previously documented. We conducted a policy Delphi study involving 24 FLOSS community managers from 22 different communities. Our panel identified 16 concerns related to managing EV in FLOSS, which we ranked by prevalence. We also describe 65 practices for managing EV in FLOSS. Almost three-quarters of these practices are used by at least three community managers. We report these practices using a systematic presentation that includes context, relationships between practices, and concerns that they address. These findings provide a coherent framework that can help FLOSS community managers to better manage episodic contributors.
Using qualitative data analysis (QDA) to perform domain analysis and modeling has shown great promise. Yet, the evaluation of such approaches has been limited to single-case case studies. While these exploratory cases are valuable for an initial assessment, the evaluation of the efficacy of QDA to solve the suggested problems is restricted by the common single-case case study research design. Using our own method, called QDAcity-RE, as the example, we present an in-depth empirical evaluation of employing qualitative data analysis for domain modeling using a controlled experiment design. Our controlled experiment shows that the QDA-based method leads to a deeper and richer set of domain concepts discovered from the data, while also being more time efficient than the control group using a comparable non-QDA-based method with the same level of traceability.