Alessandro Garcia

Filipe Falcão, Caio Barbosa, Baldoino Fonseca et al.

[This paper has been withdrawn by the author due to updated research available on arXiv (arXiv:1811.01918)] As the modern open-source paradigm makes it easier to contribute to software projects, the number of developers involved in these projects keep increasing. This growth in the amount of developers makes it more difficult to deal with harmful contributions. Recent researches have found that technical and social factors can predict the success of contributions to open-source projects on GitHub. However, these researches do not study the relation between these factors with the introduction of bugs. Our study aims at investigating the influence of technical (such as, developers' experience) and social (such as, number of followers) factors on the introduction of bugs, using information from 14 projects hosted on GitHub. Understanding the influence of these factors may be useful to developers, code reviewers and researchers. For instance, code reviewers may want to double check commits from developers that present bug-related factors. We found that technical factors have a consistent influence in the introduction of bugs. On the other hand, social factors present signs of influence in bug introduction that would require more data to be properly evaluated. Moreover, we found that perils present in the mining of GitHub may impact the factors results.

Paolo Missier, Callum McClean, Jonathan Carlton et al.

Tropical diseases like \textit{Chikungunya} and \textit{Zika} have come to prominence in recent years as the cause of serious, long-lasting, population-wide health problems. In large countries like Brasil, traditional disease prevention programs led by health authorities have not been particularly effective. We explore the hypothesis that monitoring and analysis of social media content streams may effectively complement such efforts. Specifically, we aim to identify selected members of the public who are likely to be sensitive to virus combat initiatives that are organised in local communities. Focusing on Twitter and on the topic of Zika, our approach involves (i) training a classifier to select topic-relevant tweets from the Twitter feed, and (ii) discovering the top users who are actively posting relevant content about the topic. We may then recommend these users as the prime candidates for direct engagement within their community. In this short paper we describe our analytical approach and prototype architecture, discuss the challenges of dealing with noisy and sparse signal, and present encouraging preliminary results.