The work to be performed on open source systems, whether feature developments or defects, is typically described as an issue (or bug). Developers self-select bugs from the many open bugs in a repository when they wish to perform work on the system. This paper evaluates a recommender, called NextBug, that considers the textual similarity of bug descriptions to predict bugs that require handling of similar code fragments. First, we evaluate this recommender using 69 projects in the Mozilla ecosystem. We show that for detecting similar bugs, a technique that considers just the bug components and short descriptions perform just as well as a more complex technique that considers other features. Second, we report a field study where we monitored the bugs fixed for Mozilla during a week. We sent mails to the developers who fixed these bugs, asking whether they would consider working on the recommendations provided by NextBug; 39 developers (59%) stated that they would consider working on these recommendations; 44 developers (67%) also expressed interest in seeing the recommendations in their bug tracking system.
This paper proposes an analysis of political homophily among Twitter users during the 2016 American Presidential Election. We collected 4.9 million tweets of 18,450 users and their contact network from August 2016 to November 2016. We defined six user classes regarding their sentiment towards Donald Trump and Hillary Clinton: whatever, Trump supporter, Hillary supporter, positive, neutral, and negative. Next, we analyzed their political homophily in three scenarios. Firstly, we analyzed the Twitter follow, mention and retweet connections either unidirectional and reciprocal. In the second scenario, we analyzed multiplex connections, and in the third one, we analyzed friendships with similar speeches. Our results showed that negative users, users supporting Trump, and users supporting Hillary had homophily in all analyzed scenarios. We also found out that the homophily level increase when there are reciprocal connections, similar speeches, or multiplex connections.
Background: Due to the characteristics of the maintenance process followed in open source systems, developers are usually overwhelmed with a great amount of bugs. For instance, in 2012, approximately 7,600 bugs/month were reported for Mozilla systems. Improving developers' productivity in this context is a challenging task. In this paper, we describe and evaluate the new version of NextBug, a tool for recommending similar bugs in open source systems. NextBug is implemented as a Bugzilla plug-in and it was design to help maintainers to select the next bug he/she would fix. Results: We evaluated the new version of NextBug using a quantitative and a qualitative study. In the quantitative study, we applied our tool to 130,495 bugs reported for Mozilla products, and we consider as similar bugs that were handled by the same developer. The qualitative study reports the main results we received from a survey conducted with Mozilla developers and contributors. Most surveyed developers stated their interest in working with a tool like NextBug.
Conclusion:We achieved the following results in our evaluation: (i) NextBug was able to provide at least one recommendation to 65% of the bugs in the quantitative study, (ii) in 54% of the cases there was at least one recommendation among the top-3 that was later handled by the same developer; (iii) 85% of Mozilla developers stated that NextBug would be useful to the Mozilla community.
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