Megan Squire

Should we move to stack overflow?: measuring the utility of social media for developer support

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.

How the FLOSS Research Community Uses Email Archives

Artifacts of the software development process, such as source code or emails between developers, are a frequent object of study in empirical software engineering literature. One of the hallmarks of free, libre, and open source software (FLOSS) projects is that the artifacts of the development process are publicly-accessible and therefore easily collected and studied. Thus, there is a long history in the FLOSS research community of using these artifacts to gain understanding about the phenomenon of open source software, which could then be compared to studies of software engineering more generally. This paper looks specifically at how the FLOSS research community has used email artifacts from free and open source projects. It provides a classification of the relevant literature using a publicly-available online repository of papers about FLOSS development using email. The outcome of this paper is to provide a broad overview for the software engineering and FLOSS research communities of how other researchers have used FLOSS email message artifacts in their work.

Describing the Software Forge Ecosystem

Code forges are online software systems that are designed to support teams doing software development work. There have been few if any attempts in the research literature to describe the web of people, projects, and tools that make up the free, libre, and open source (FLOSS) forge ecosystem. The main contributions of this paper are (1) to introduce a classification of FLOSS-oriented forges according to their characteristics, (2) to describe the forge-level and project-level data and artifacts currently available at each FLOSS forge, (3) to show various patterns already discovered in the FLOSS forge ecosystem, such as timelines of creation or arrangements by size or feature, (4) to make some recommendations to forge providers and data collectors about how to expose the structure and information in the forges, and (5) to describe the effort needed to extend our publicly-available information about the FLOSS forge ecosystem into the future.

FLOSS as a Source for Profanity and Insults: Collecting the Data

An important task in machine learning and natural language processing is to learn to recognize different types of human speech, including humor, sarcasm, insults, and profanity. In this paper we describe our method to produce test and training data sets to assist in this task. Our test data sets are taken from the domain of free, libre, and open source software (FLOSS) development communities. We describe our process in constructing helper sets of relevant data, such as profanity lists, lists of insults, and lists of projects with their codes of conduct. Contributions of this paper are to describe the background literature on computer-aided methods of recognizing insulting or profane speech, to describe the parameters of data sets that are useful in this work, and to outline how FLOSS communities are such a rich source of insulting or profane speech data. We then describe our data sets in detail, including how we created these data sets, and provide some initial guidelines for usage.

Integrating Projects from Multiple Open Source Code Forges

Much of the data about free, libre, and open source (FLOSS) software development comes from studies of code forges or code repositories used for managing projects. This paper presents a method for integrating data about open source projects by way of matching projects (entities) across multiple code forges. After a review of the relevant literature, a few of the methods are chosen and applied to the FLOSS domain, including a comparison of some simple scoring systems for pairwise project matches. Finally, the paper describes limitations of this approach and recommendations for future work.

Data sets: the circle of life in ruby hosting, 2003-2015

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 repositorys 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.

A Bit of Code: How the Stack Overflow Community Creates Quality Postings

The Stack Overflow web site is an online community where programmers can ask and answer one anothers questions, earning points and badges. The site offers guidance in the form of a Frequently Asked Questions (FAQ), beginning with “What kind of questions can I ask here?” The answer explains that “the best Stack Overflow questions have a bit of source code in them”. This paper explores the role of source code and non-source code text on Stack Overflow in both questions and answers. The primary contribution of this paper is to provide a more detailed understanding of whether the presence of source code (and how much) actually will produce the “best” Stack Overflow questions or answers. A second contribution of this paper is to determine how the non-code portions of the text might also contribute the “best” Stack Overflow postings.

Project roles in the Apache Software Foundation: A dataset

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.

VisMap: Exploratory Visualization Support for Introductory Data Science and Visualization

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.

Differentiating Communication Styles of Leaders on the Linux Kernel Mailing List

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.