Lessons Learned from Applying Social Network Analysis on an Industrial Free/Libre/Open Source Software Ecosystem

Many software projects are no longer done in-house by a single organization. Instead, we are in a new age where software is developed by a networked community of individuals and organizations, which base their relations to each other on mutual interest. Paradoxically, recent research suggests that software development can actually be jointly-developed by rival firms. For instance, it is known that the mobile-device makers Apple and Samsung kept collaborating in open source projects while running expensive patent wars in the court. Taking a case study approach, we explore how rival firms collaborate in the open source arena by employing a multi-method approach that combines qualitative analysis of archival data (QA) with mining software repositories (MSR) and Social Network Analysis (SNA). While exploring collaborative processes within the OpenStack ecosystem, our research contributes to Software Engineering research by exploring the role of groups, sub-communities and business models within a high-networked open source ecosystem. Surprising results point out that competition for the same revenue model (i.e., operating conflicting business models) does not necessary affect collaboration within the ecosystem. Moreover, while detecting the different sub-communities of the OpenStack community, we found out that the expected social tendency of developers to work with developers from same firm (i.e., homophily) did not hold within the OpenStack ecosystem. Furthermore, while addressing a novel, complex and unexplored open source case, this research also contributes to the management literature in coopetition strategy and high-tech entrepreneurship with a rich description on how heterogeneous actors within a high-networked ecosystem (involving individuals, startups, established firms and public organizations) joint-develop a complex infrastructure for big-data in the open source arena.Comment: As accepted by the Journal of Internet Services and Applications (JISA

Towards Identifying Paid Open Source Developers - A Case Study with Mozilla Developers

Open source development contains contributions from both hired and volunteer software developers. Identification of this status is important when we consider the transferability of research results to the closed source software industry, as they include no volunteer developers. While many studies have taken the employment status of developers into account, this information is often gathered manually due to the lack of accurate automatic methods. In this paper, we present an initial step towards predicting paid and unpaid open source development using machine learning and compare our results with automatic techniques used in prior work. By relying on code source repository meta-data from Mozilla, and manually collected employment status, we built a dataset of the most active developers, both volunteer and hired by Mozilla. We define a set of metrics based on developers' usual commit time pattern and use different classification methods (logistic regression, classification tree, and random forest). The results show that our proposed method identify paid and unpaid commits with an AUC of 0.75 using random forest, which is higher than the AUC of 0.64 obtained with the best of the previously used automatic methods.Comment: International Conference on Mining Software Repositories (MSR) 201

Towards understanding open-coopetition – Lessons from the automotive industry

Products are often co-developed in networks that embed multiple organizations. Paradoxically, such product development networks can tie rival and competing firms that cooperate with each other in an open-source way. The management of such modus operandi, where firms give up some intellectual property rights granted by law and work with competitors in an open-source way, can be very challenging as it can lead to commoditization, free-riding, and unintended spillover effects. Building upon extant knowledge in coopetition, open-source software, product development, and innovation, we conducted an exploratory case study aimed at understanding open-coopetition (i.e., cooperation among competitors in an open-source way) in the automotive industry. To do so, we leveraged publicly available naturally occurring digital data and qualitative interviews pertaining to four coopetitive open-source projects. Out preliminary results highlight the increasing complexity of the software that powers modern cars and consequent convergence of the automotive industry with the software industry

Open Source Is A-Changin': How Qualitative Research Can Help Us Adapt

In the past five years, industry has overwhelmingly turned to open source as a primary means to build products and services atop of, with around 80\% of surveyed companies acknowledging that they run on open source. 66\% of companies say they would first consider open source options before proprietary ones. A decade ago, this wasn't the case at all; these numbers were flipped, with a tiny minority of companies preferring open source solutions. This has changed the dynamics of open source community markedly. In the early days of the open source movement, most people using a project were also contributing back to it in some way. However, the number of users versus contributors has changed by likely uncountable orders of magnitude. With unicorn startups being built on top of this open source shared infrastructure, the incentives and motives behind open source contributors building this valuable infrastructure has also begun to shift. Meanwhile, software engineering studies focused on open source have been overwhelmingly quantitative; quantitative analyses on codebases, issue trackers, surveys, etc. In this essay, we argue that with these changing tides comes dynamics that we can't easily quantify. Why then do rely predominantly on quantitative methods when we attempt to understand the dynamics of open source communities? We lay out a number of research questions and qualitative techniques from the social sciences that can help us better understand these trends, and how to adapt to them going forward