Online division of labour: emergent structures in Open Source Software

Abstract

The development Open Source Software fundamentally depends on the participation and commitment of volunteer developers to progress on a particular task. Several works have presented strategies to increase the on-boarding and engagement of new contributors, but little is known on how these diverse groups of developers self-organise to work together. To understand this, one must consider that, on one hand, platforms like GitHub provide a virtually unlimited development framework: any number of actors can potentially join to contribute in a decentralised, distributed, remote, and asynchronous manner. On the other, however, it seems reasonable that some sort of hierarchy and division of labour must be in place to meet human biological and cognitive limits, and also to achieve some level of efficiency. These latter features (hierarchy and division of labour) should translate into detectable structural arrangements when projects are represented as developer-file bipartite networks. Thus, in this paper we analyse a set of popular open source projects from GitHub, placing the accent on three key properties: nestedness, modularity and in-block nestedness –which typify the emergence of heterogeneities among contributors, the emergence of subgroups of developers working on specific subgroups of files, and a mixture of the two previous, respectively. These analyses show that indeed projects evolve into internally organised blocks. Furthermore, the distribution of sizes of such blocks is bounded, connecting our results to the celebrated Dunbar number both in off- and on-line environments. Our conclusions create a link between bio-cognitive constraints, group formation and online working environments, opening up a rich scenario for future research on (online) work team assembly (e.g. size, composition, and formation). From a complex network perspective, our results pave the way for the study of time-resolved datasets, and the design of suitable models that can mimic the growth and evolution of OSS projects.

Macro- and mesoscale pattern interdependencies in complex networks

“…The red line in ( a ) indicates the average values of Q for a given N, the error shaded area represents one standard deviation above and below that average; networks with B = 1 are excluded in this computation. ( b ) Results obtained for the set of 57 unipartite social network analysed in [39]; and ( c ) for the set of bipartite social and ecological networks [59,60]. In these two panels, dark blue dots represent the real Q value after optimization, and bars represent the corresponding estimated upper and lower bounds for the same network.…”

“…the assumption of homogeneous sizes of communities or uncorrelated noise. To assess the accuracy that our development has in real scenarios, we perform experiments on 347 real networks, covering several domains: 57 real unipartite networks [39] (mostly social and economic networks) and 290 bipartite networks (ecological in most cases [59], with some social networks [60] as well).…”

Investigating Power Relations in Open Source Software Ecosystems

“…The motivations to contribute with OSS have been changing recently, focusing more on learning, career, and payment motivations [Gerosa et al 2021]. However, this is not the only change: OSS presents a nearly hierarchical structure composed of the roles and privileges each developer has within a project [Palazzi et al 2019].…”

On the analysis of non-coding roles in open source development

“…As an example, they influence the acceptance of pull requests (Tsay et al 2014a, b;Casalnuovo et al 2015) depending, among other things, of the connection between the submitter and other core members. Internal community dynamics are also useful, for instance, to determine how committers efforts are distributed over the project files (Palazzi et al 2019), to help in the discovery of implicit subteams/subsystems in the project (Ashraf et al 2020), facilitate the onboarding of newcomers (Steinmacher et al 2019) or its socialization (Carillo et al 2017). Analysis of internal dynamics can also be used to identify the most active members (Gasparini et al 2019) and leaders (Li et al 2012) or as a way to predict the future contributions of project members (Decan et al 2020).…”