Software engineering is a human-centered activity involving various stakeholders with different backgrounds that have to communicate and collaborate to reach shared objectives. The emergence of conflicts among stakeholders may lead to undesired effects on software maintainability, yet it is often unavoidable in the long run. Community smells, i.e., sub-optimal communication and collaboration practices, have been defined to map recurrent conflicts among developers. While some community smell detection tools have been proposed in the recent past, these can be mainly used for research purposes because of their limited level of usability and user engagement. To facilitate a wider use of community smell-related information by practitioners, we present CADOCS, a client-server conversational agent that builds on top of a previous community smell detection tool proposed by Almarini et al. to (1) make it usable within a well-established communication channel like Slack and (2) augment it by providing initial support to software analytics instruments useful to diagnose and refactor community smells. We describe the features of the tool and the preliminary evaluation conducted to assess and improve robustness and usability.

Estimating and understanding productivity still represents a crucial task for researchers and practitioners. Researchers spent significant effort identifying the factors that influence software developers' productivity, providing several approaches for analyzing and predicting such a metric. Although different works focused on evaluating the impact of human factors on productivity, little is known about the influence of cultural/geographical diversity in software development communities. Indeed, in previous studies, researchers treated cultural aspects like an abstract concept without providing a quantitative representation. This work provides an empirical assessment of the relationship between cultural and geographical dispersion of a development community—namely, how diverse a community is in terms of cultural attitudes and geographical collocation of the members who belong to it—and its productivity. To reach our aim, we built a statistical model that contained product and socio-technical factors as independent variables to assess the correlation with productivity, i.e., the number of commits performed in a given time. Then, we ran our model considering data of 25 open-source communities on GitHub. Results of our study indicate that cultural and geographical dispersion impact productivity, thus encouraging managers and practitioners to consider such aspects during all the phases of the software development lifecycle.

Software development is de facto a social activity that often involves people from all places to join forces globally. In such common instances, project managers must face social challenges, e.g., personality conflicts and language barriers, which often amount literally to “culture shock”. In this paper, we seek to analyze and illustrate how cultural and geographical dispersion—that is, how much a community is diverse in terms of its members’ cultural attitudes and geographical collocation—influence the emergence of collaboration and communication problems in open-source communities, a.k.a. community smells, the socio-technical precursors of unforeseen, often nasty organizational conditions amounting collectively to the phenomenon called social debt. We perform an extensive empirical study on cultural characteristics of GitHub developers, and build a regression model relating the two types of dispersion—cultural and geographical—with the emergence of four types of community smells, i.e., Organizational Silo, Lone Wolf, Radio Silence, and Black Cloud. Results indicate that cultural and geographical factors influence collaboration and communication within open-source communities, to an extent which incites—or even more interestingly mitigates, in some cases—community smells, e.g., Lone Wolf, in development teams. Managers can use these findings to address their own organizational structure and tentatively diagnose any nasty phenomena related to the conditions under study.

Test smells represent sub-optimal design or implementation solutions applied when developing test cases. Previous research has shown that these smells may decrease both maintainability and effectiveness of tests and, as such, researchers have been devising methods to automatically detect them. Nevertheless, there is still a lack of tools that developers can use within their integrated development environment to identify test smells and refactor them. In this paper, we present DARTS (Detection And Refactoring of Test Smells), an Intellij plug-in which (1) implements a state-of-the-art detection mechanism to detect instances of three test smell types, ie, General Fixture, Eager Test, and Lack of Cohesion of Test Methods, at commit-level and (2) enables their automated refactoring through the integrated APIs provided by Intellij.