Their paper describes how to use text mining and machine learning to identify differences in the communication styles of two of the lead developers of the Linux kernel. Using the text of 40,000 email messages over 20 years, the authors developed a program that could automatically distinguish between the writing styles of these two individuals with 96 percent accuracy. The results of this study can help researchers understand the culture of the Linux community, and why there is occasional controversy regarding differences in communication styles on the email mailing list for that important software project.

The full citation of the paper follows:

Schneider, D., Spurlock, S., and Squire, M. (2016). Differentiating Communication Styles of Leaders on the Linux Kernel Mailing List. In Proceedings of the 12th International Symposium on Open Collaboration (OpenSym 2016). Berlin, Germany. August 17-19. pp 101-110. []

This prize was awarded for her competitively-refereed paper and the accompanying longitudinal data sets she created. To create these data sets, Squire spent more than ten years collecting data about projects and programmers who used the Ruby language and the RubyForge and RubyGems hosting facilities. She then cleaned the data, created documentation for it, and hosted it in her library of 68,000 other public data sets she has created to describe how open source software is made.

The full citation of the paper is below:

Squire, M. (2016). “Data Sets: The Circle of Life in Ruby Hosting, 2003-2015”. In Proceedings of the 13th International Conference on Mining Software Repositories (MSR ’16). IEEE. May 14-15. Austin, TX. 452-455.

This competitively-refereed article features the use of data mining techniques to answer the practical question of where to locate software development support services. To answer this question, Squire harvested more than 190 million records from the popular programmer web site Stack Overflow, and then compared the response time and participation rates on this site compared to older social media technology (mailing lists and web forums). She discovered that response time is much faster on Stack Overflow, but the verdict on whether participation rates are higher is mixed.

The full citation of the paper is as follows:

Squire, M. (2015). “Should We Move to Stack Overflow?” Measuring the Utility of Social Media for Developer Support. In Proceedings of the 37th International Conference on Software Engineering (ICSE 2015). IEEE. May 20-22. Florence, Italy.

This paper described how a new invention, the “pastebin,” is used to streamline software developer communications. They collected data about how several dozen pastebin tools were used on 9,000 email mailing lists to determine that pastebin adoption followed the traditional S-curve of classic Diffusion of Innovation (DoI) theory. In addition, they revealed how adoption of the innovation differed between variants of the tool, and how and why some sub-communities rejected the innovation.

The full citation of the paper follows:

Squire, M. and Smith, A. (2015). The diffusion of pastebin tools to enhance communication in FLOSS mailing lists. In Damiani, E., F. Flavi, D. Riehle, A. Wasserman (Eds.) Open Source Systems: Adoption and Impact (Proceedings of the 11th International Conference on Open Source Systems OSS2015). IFIP. Florence, Italy. May 16. 45-57. []

The abstract reads:

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.

The full citation is below:

Squire, M. and Gazda, R. (2015). FLOSS as a Source for Profanity and Insults: Collecting the Data. In Proceedings of the 48th Hawaii International Conference on System Sciences (HICSS48). IEEE. Kauai, HI, USA. January 5-8.5290-5298.

The conference hosted more than 1,000 professional software developers and managers working on open source software at companies such as Red Hat, Cisco and IBM.

The panel session was moderated by DeLisa Alexander, executive vice president and chief people officer at Red Hat, and addressed questions such as why female under-representation in technology exists, why it’s important that more females choose to go into technology and open source, and what can be done to encourage it.

“We cannot find enough qualified people to do the important jobs that we have to do,” Alexander said in opening the session. “Half the world’s population is female, and in the U.S. workforce, 47 percent are female. Then you look at computing jobs and only 25 percent of those jobs are held by women. If you look at the number of people who are entering degree fields for computer science and information science that are female, it’s 12 percent.”

Squire drew applause when she explained that while plenty of studies have shown that the reaching girls in middle school and filling the pipeline is important, it is equally important to study the “leaky pipeline”: why do women leave technology and not return? 

In a separate session, Squire also introduced her research on how and why the academic community studies open source software.

The “All Things Open” conference was held Oct. 22-23, 2014, at the Raleigh Downtown Convention Center.

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The three-year project is under the direction of Associate Professor Megan Squire in the Department of Computing Sciences, who serves as its principal Investigator.                                 

The award will be used to integrate, expand and enhance several distinct data sources currently used by three research communities: those who study free, libre and open source software (FLOSS), the larger empirical software engineering research community, and researchers engaged in data mining and text mining.

The goal is to build an infrastructure that will enable even more advanced analysis in the fields of empirical software engineering and data mining/text mining. This project will provide high-quality, very large collections of real developer communication artifacts, and the tools to analyze them and share results.

The textual data collected will be some of the largest curated collections of semi-structured and unstructured text available anywhere for public use, including new data sources never before collected and curated. 

This award starts Sept. 1, 2014, and ends Aug. 31, 2017. 

In the first paper, Squire gives an overview of three new artifacts that emprical software engineering researchers can use to study how free, libre, and open source software (FLOSS) is made. The main findings were that Github, Stack Overflow, and paste sites (like Pastebin, Github gist, and JSFiddle) are critical new tools for the development of FLOSS, and the artifacts from these tools can be collected and studied using similar techniques to what we have done in the past.

In the second paper, Squire and Funkhouser proposed a novel way of mining the text data from the StackOverflow developer question-and-answer web site. They were interested in determining programmatically which text metrics will lead to higher quality postings. The main findings were that a higher code-to-text ratio matters in formulating answers more than in formulating questions, and that readability does not matter as much as sentiment in constructing high-quality postings.

The full citations are below:

Squire, M. (2014). Forge++: The Changing Landscape of FLOSS Development. In Proceedings of the 47th Hawaii International Conference on System Sciences (HICSS47). IEEE. Big Island, HI, USA. 3266-3275. DOI: 10.1109/HICSS.2014.405

Squire, M. and Funkhouser, C. (2014). “A Bit of Code”: How the Stack Overflow Community Creates Quality Postings. In Proceedings of the 47th Hawaii International Conference on System Sciences (HICSS47). IEEE. Big Island, HI, USA. 1425-1434. DOI: 10.1109/HICSS.2014.185

This paper was presented Oct. 9 at the 2013 Third International Workshop on Replication in Empirical Software Engineering Research (RESER2013), a gathering of scholars who ensure replicability in the empirical studies of how software is made.

The conference was held in Baltimore, Md., in conjunction with Empirical Software Engineering International Week, an international gathering of researchers who use empirical techniques to study software engineering process. The conference proceedings are published by IEEE.

As described in her papers, Project Roles in the Apache Software Foundation: A Dataset [1], and Apache-Affiliated Twitter Screen Names: A Dataset [2], Associate Professor Megan Squire wrote software to collect and curated two novel sets of data which were then donated back to the research community as part of the FLOSSmole data commons.

These papers were presented May 19 at the 10th Mining Software Repositories Conference, a gathering of scholars who analyze large collections of software artifacts for empirical data about how the software is made.

In the Project Roles paper, Squire describes the process for determing the roles (leader, developer, committer, contributor, etc) for more than 5000 software developers within the ASF itself and more than 200 of its affiliate projects.

In the Twitter Names paper, Squire describes how she wrote software to automatically collect the Twitter screen names of Apache-affiliated developers. This data is useful for any researcher who wishes to study how Twitter is being used to create software.

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