Code forges are third party software repositories that also provide various tools and facilities for distributed software development teams to use, including source code control systems, mailing lists and communication forums, bug tracking systems, web hosting space, and so on. The main contributions of this paper are to present some new data sets relating to the technology adoption lifecycles of a group of six free, libre, and open source software (FLOSS) code forges, and to compare the lifecycles of the forges to each other and to the model presented by classical Diffusion of Innovation (DoI) theory. We find that the observed adoption patterns of code forges rarely follow the DoI model, especially as larger code forges are beset by spam and abuse. The only forge exhibiting a DoI-like lifecycle was a smaller, community-managed, special-purpose forge whose demise was planned in advance. The results of this study will be useful in explaining adoption trajectories, both to practitioners building collaborative FLOSS ecosystems and to researchers who study the evolution and adoption of socio-technical systems.

Studying software repositories and hosting services can provide valuable insights into the behaviors of large groups of software developers and their projects. Traditionally, most analysis of metadata collected from hosting services has been conducted by specifying some short window of time, typically just a few years. To date, few - if any - studies have been built from data comprising the entirety of a repository's lifespan: from its birth to its death, and rebirth. Thus, the first contribution of this data set is to support the historical analysis of over ten years of collected metadata from the now-defunct RubyForge project hosting site, as well as the follow-on successor to RubyForge, the RubyGems hosting facility. The data sets and sample analyses in this paper will be relevant to researchers studying both software evolution and the distributed software development process.

We present VisMap, a Web-based software tool that supports student exploration of possible data visualizations during a typical process of data science practice. Specifically, we detail visualization approaches within three major kinds of data analysis (part-to-whole and rank, correlation, and geospatial) and discuss how VisMap allows students to visually explore visualization options that correspond to the kind of analysis. The discussion is rooted in our experiences developing an introductory undergraduate course in data science and in our classroom observations of the limitations of existing software tools. These limitations include inefficiency of visualization comparison, difficulty in manipulating data to achieve visualization goals, and current challenges novices face when using professional tools simultaneous with learning about visualization. We finally offer a variety of future paths to further evaluate and refine VisMap.

Much communication between developers of free, libre, and open source software (FLOSS) projects happens on email mailing lists. Geographically and temporally dispersed development teams use email as an asynchronous, centralized, persistently stored institutional memory for sharing code samples, discussing bugs, and making decisions. Email is especially important to large, mature projects, such as the Linux kernel, which has thousands of developers and a multi-layered leadership structure. In this paper, we collect and analyze data to understand the communication patterns in such a community. How do the leaders of the Linux Kernel project write in email? What are the salient features of their writing, and can we discern one leader from another? We find that there are clear written markers for two leaders who have been particularly important to recent discussions of leadership style on the Linux Kernel Mailing List (LKML): Linux Torvalds and Greg Kroah-Hartman. Furthermore, we show that it is straightforward to use a machine learning strategy to automatically differentiate these two leaders based on their writing. Our findings will help researchers understand how this community works, and why there is occasional controversy regarding differences in communication styles on the LKML.

Stack Overflow is an enormously popular question-and-answer web site intended for software developers to help each other with programming issues. Some software projects aimed at developers (for example, application programming interfaces, application engines, cloud services, development frameworks, and the like) are closing their self-supported developer discussion forums and mailing lists and instead directing developers to use special-purpose tags on Stack Overflow. The goals of this paper are to document the main reasons given for moving developer support to Stack Overflow, and then to collect and analyze data from a group of software projects that have done this, in order to show whether the expected quality of support was actually achieved. The analysis shows that for all four software projects in this study, two of the desired quality indicators, developer participation and response time, did show improvements on Stack Overflow as compared to mailing lists and forums. However, we also found several projects that moved back from Stack Overflow, despite achieving these desired improvements. The results of this study are applicable to a wide variety of software projects that provide developer support using social media.

This paper outlines the steps in the creation and maintenance of a new dataset listing leaders of the various projects of the Apache Software Foundation (ASF). Included in this dataset are different levels of committers to the various ASF project code bases, as well as regular and emeritus members of the ASF, and directors and officers of the ASF. The dataset has been donated to the FLOSSmole project under an open source license, and is available for download (https://code.google.com/p/flossmole/downloads/detail?name=apachePeople2013-Jan.zip), or for direct querying via a database client.

This paper describes a new dataset containing Twitter screen names for members of the projects affiliated with the Apache Software Foundation (ASF). The dataset includes the confirmed Twitter screen names, as well as the real name as listed on Twitter, and the user identification as used within the Apache organization. The paper also describes the process used to collect and clean this data, and shows some sample queries for learning how to use the data. The dataset has been donated to the FLOSSmole project and is available for download (https://code.google.com/p/flossmole/downloads/detail?name=apacheTwitter20 13-Jan.zip) or direct querying via a database client.