Marcelo Serrano Zanetti

The Role of Emotions in Contributors Activity: A Case Study on the GENTOO Community

We analyze the relation between the emotions and the activity of contributors in the Open Source Software project GENTOO. Our case study builds on extensive data sets from the projects bug tracking platform BUGZILLA, to quantify the activity of contributors, and its mail archives, to quantify the emotions of contributors by means of sentiment analysis. The GENTOO project is known for a period of centralization within its bug triaging community. This was followed by considerable changes in community organization and performance after the sudden retirement of the central contributor. We analyze how this event correlates with the negative emotions, both in bilateral email discussions with the central contributor, and at the level of the whole community of contributors. We then extend our study to consider the activity patterns of GENTOO contributors in general. We find that contributors are more likely to become inactive when they express strong positive or negative emotions in the bug tracker, or when they deviate from the expected value of emotions in the mailing list. We use these insights to develop a Bayesian classifier that detects the risk of contributors leaving the project. Our analysis opens new perspectives for measuring online contributor motivation by means of sentiment analysis and for real-time predictions of contributor turnover in Open Source Software projects.

The co-evolution of socio-technical structures in sustainable software development: lessons from the open source software communities

Software development depends on many factors, including technical, human and social aspects. Due to the complexity of this dependence, a unifying framework must be defined and for this purpose we adopt the complex networks methodology. We use a data-driven approach based on a large collection of open source software projects extracted from online project development platforms. The preliminary results presented in this article reveal that the network perspective yields key insights into the sustainability of software development.

The rise and fall of a central contributor: Dynamics of social organization and performance in the GENTOO community

Social organization and division of labor crucially influence the performance of collaborative software engineering efforts. In this paper, we provide a quantitative analysis of the relation between social organization and performance in Gentoo, an Open Source community developing a Linux distribution. We study the structure and dynamics of collaborations as recorded in the projects bug tracking system over a period of ten years. We identify a period of increasing centralization after which most interactions in the community were mediated by a single central contributor. In this period of maximum centralization, the central contributor unexpectedly left the project, thus posing a significant challenge for the community. We quantify how the rise, the activity as well as the subsequent sudden dropout of this central contributor affected both the social organization and the bug handling performance of the Gentoo community. We analyze social organization from the perspective of network theory and augment our quantitative findings by interviews with prominent members of the Gentoo community which shared their personal insights.

A Quantitative Study of Social Organisation in Open Source Software Communities

The success of open source projects crucially depends on the voluntary contributions of a sufficiently large community of users. Apart from the mere size of the community, interesting questions arise when looking at the evolution of structural features of collaborations between community members. In this article, we discuss several network analytic proxies that can be used to quantify different aspects of the social organisation in social collaboration networks. We particularly focus on measures that can be related to the cohesiveness of the communities, the distribution of responsibilities and the resilience against turnover of community members. We present a comparative analysis on a large-scale dataset that covers the full history of collaborations between users of

Automated software remodularization based on move refactoring: a complex systems approach

14

Categorizing bugs with social networks: a case study on four open source software communities

major open source software communities. Our analysis covers both aggregate and time-evolving measures and highlights differences in the social organisation across communities. We argue that our results are a promising step towards the definition of suitable, potentially multi-dimensional, resilience and risk indicators for open source software communities.

A network perspective on software modularity

Modular design is a desirable characteristic of complex software systems that can significantly improve their comprehensibility, maintainability and thus quality. While many software systems are initially created in a modular way, over time modularity typically degrades as components are reused outside the context where they were created. In this paper, we propose an automated strategy to remodularize software based on move refactoring, i.e. moving classes between packages without changing any other aspect of the source code. Taking a complex systems perspective, our approach is based on complex networks theory applied to the dynamics of software modular structures and its relation to an n-state spin model known as the Potts Model. In our approach, nodes are probabilistically moved between modules with a probability that nonlinearly depends on the number and module membership of their adjacent neighbors, which are defined by the underlying network of software dependencies. To validate our method, we apply it to a dataset of 39 Java open source projects in order to optimize their modularity. Comparing the source code generated by the developers with the optimized code resulting from our approach, we find that modularity (i.e. quantified in terms of a standard measure from the study of complex networks) improves on average by 166+-77 percent. In order to facilitate the application of our method in practical studies, we provide a freely available Eclipse plug-in.