Impacts of software community patterns on process and product: An empirical study

Abstract

Software engineering projects are now more than ever a community effort. In the recent past, researchers have shown that their success not only depends on source code quality, but also on other aspects like the balance of power distance, culture, and global engineering practices, and more. In such a scenario, understanding the characteristics of the community around a project and foresee possible problems may be the key to develop successful systems. In this paper, we focus on this research problem and propose an exploratory study on the relation between community patterns, i.e., recurrent mixes of organizational or social structure types, and aspects related to the quality of software products and processes by mining open-source software repositories hosted on GitHub . We first exploit association rule mining to discover frequent relations between community pattern and community smells, i.e., sub-optimal patterns across the organizational structure of a software development community that may be precursors of some form of social debt. Further on, we use statistical analyses to understand their impact on software maintainability and on the community engagement, in terms of contributions and issues. Our findings show that different organizational patterns are connected to different forms of socio-technical problems; further on, specific combinations are set in equally specific contextual conditions. Findings support two possible conclusions: (1) practitioners should put in place specific preventive actions aimed at avoiding the emergence of community smells and (2) such actions should be drawn according to the contextual conditions of the organization and the project. • A list of association rules between community patterns and community smells to see how they relate each other. • An empirical analysis on how community patterns affect the product quality. • An empirical analysis on how community patterns affect the development process. • A replication package containing all the materials to reproduce and extend our study.