How the FLOSS Research Community Uses Email Archives

Squire, Megan

doi:10.4018/jossp.2012010103

Cited by 11 publications

(5 citation statements)

References 17 publications

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“…Our work touches upon different fields of research. First, mail archives have been studied since as early as 2006 (see recent review by Squire [33]), e.g., to understand knowledge sharing [32] and how users of the software get help [31].…”

Section: Related Workmentioning

confidence: 99%

How social Q&A sites are changing knowledge sharing in open source software communities

Vasilescu

Serebrenik

Dévanbu

et al. 2014

Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work &Amp; Social Computing

183

103

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Historically, mailing lists have been the preferred means for coordinating development and user support activities. With the emergence and popularity growth of social Q&A sites such as the StackExchange network (e.g., StackOverflow), this is beginning to change. Such sites offer different sociotechnical incentives to their participants than mailing lists do, e.g., rich web environments to store and manage content collaboratively, or a place to showcase their knowledge and expertise more visibly to peers or potential recruiters. A key difference between StackExchange and mailing lists is gamification, i.e., StackExchange participants compete to obtain reputation points and badges. Using a case study of R, a popular data analysis software, in this paper we investigate how mailing list participation has evolved since the launch of StackExchange. Our main contribution is assembling a joint data set from the two sources, in which participants in both the r-help mailing list and StackExchange are identifiable. This allows for linking their activities across the two resources and also over time. With this data set we found that user support activities are showing a strong shift away from r-help. In particular, mailing list experts are migrating to StackExchange, where their behaviour is different. First, participants active both on r-help and on StackExchange are more active than those who focus exclusively on only one of the two. Second, they provide faster answers on StackExchange than on r-help, suggesting they are motivated by the gamified environment. To our knowledge, our study is the first to directly chart the changes in behaviour of specific contributors as they migrate into gamified environments, and has important implications for knowledge management in software engineering.

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Section: Related Workmentioning

confidence: 99%

How social Q&A sites are changing knowledge sharing in open source software communities

Vasilescu

Serebrenik

Dévanbu

et al. 2014

Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work &Amp; Social Computing

183

103

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“…The increasing popularity of pastebins necessarily changes the way FLOSS artifact mining will have to happen, especially in terms of mining email messages from mailing lists. (See [37] for a review of how FLOSS researchers mine email archives.) Consider the partial email exchange shown in Figure 5.…”

Section: Pastebin Miningmentioning

confidence: 99%

Forge++: The Changing Landscape of FLOSS Development

Squire

2014

2014 47th Hawaii International Conference on System Sciences

Self Cite

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Software forges are centralized online systems that provide useful tools to help distributed development teams work together, especially in free, libre, and open source software (FLOSS). Forge-provided tools may include web space, version control systems, mailing lists and communication forums, bug tracking systems, file downloads, wikis, and the like. Empirical software engineering researchers can mine the artifacts from these tools to better understand how FLOSS is made. As the landscape of distributed software development has grown and changed, the tools needed to make FLOSS have changed as well. There are three newer tools at the center of FLOSS development today: distributed version control based forges (like Github), programmer question-and-answer communities (like Stack Overflow), and pastebin tools (like Gist or Pastebin.com). These tools are extending and changing the toolset used for FLOSS development, and redefining what a software forge looks like. The main contributions of this paper are to describe each of these tools, to identify the data and artifacts available for mining from these tools, and to outline some of the ways researchers can use these artifacts to continue to understand how FLOSS is made.

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“…A comparison has also been done in FLOSSmole and FLOSSMetrics (Gonzalez-Barahona et al, 2010). Further, author examined the literature to determine the use of email archive in conducting the study in FLOSS (Squire, 2012).…”

Section: Data Collection and Identification Of Projects/componentsmentioning

confidence: 99%

An Empirical Comparison of Machine Learning Techniques in Predicting the Bug Severity of Open and Closed Source Projects

Chaturvedi

Singh

2012

International Journal of Open Source Software and Processes

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Bug severity is the degree of impact that a defect has on the development or operation of a component or system, and can be classified into different levels based on their impact on the system. Identification of severity level can be useful for bug triager in allocating the bug to the concerned bug fixer. Various researchers have attempted text mining techniques in predicting the severity of bugs, detection of duplicate bug reports and assignment of bugs to suitable fixer for its fix. In this paper, an attempt has been made to compare the performance of different machine learning techniques namely Support vector machine (SVM), probability based Naïve Bayes (NB), Decision Tree based J48 (A Java implementation of C4.5), rule based Repeated Incremental Pruning to Produce Error Reduction (RIPPER) and Random Forests (RF) learners in predicting the severity level (1 to 5) of a reported bug by analyzing the summary or short description of the bug reports. The bug report data has been taken from NASA's PITS (Projects and Issue Tracking System) datasets as closed source and components of Eclipse, Mozilla & GNOME datasets as open source projects. The analysis has been carried out in RapidMiner and STATISTICA data mining tools. The authors measured the performance of different machine learning techniques by considering (i) the value of accuracy and F-Measure for all severity level and (ii) number of best cases at different threshold level of accuracy and F-Measure.

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How the FLOSS Research Community Uses Email Archives

Cited by 11 publications

References 17 publications

How social Q&A sites are changing knowledge sharing in open source software communities

How social Q&A sites are changing knowledge sharing in open source software communities

Forge++: The Changing Landscape of FLOSS Development

An Empirical Comparison of Machine Learning Techniques in Predicting the Bug Severity of Open and Closed Source Projects

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