Investigating probabilistic sampling approaches for large-scale surveys in software engineering
Keywords:
Population, Sampling frame, Experimental software engineering, Hierarchical clustering analysis, Stratified sampling, Survey, Multivariate analysis, Graph theory, Strongly connected componentsAbstract
BackgroundEstablishing representative samples for Software Engineering surveys is still considered a challenge. Specialized literature often presents limitations on interpreting surveys results, mainly due to the use of sampling frames established by convenience and non-probabilistic criteria for sampling from them. In this sense, we argue that a strategy to support the systematic establishment of sampling frames from an adequate source of sampling can contribute to improve this scenario.;
MethodA conceptual framework for supporting large scale sampling in Software Engineering surveys has been organized after performing a set of experiences on designing such strategies and gathering evidence regarding their benefits. The use of this conceptual framework based on a sampling strategy developed for supporting the replication of a survey on characteristics of agility and agile practices in software processes is depicted in this paper.;
ResultA professional social network (Linkedln; Conclusion
The heterogeneity and number of participants in this replication contributed to improve the strength of original surveys results. Therefore, we believe the sharing of this experience, the instruments and plan can be helpful for those researchers and practitioners interested on executing large scale surveys in Software Engineering.;
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Copyright (c) 2021 Rafael Maiani de Mello, Pedro Corrêa da Silva, Guilherme Horta Travassos
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