The Internet has provided people with new ways of expressing not only their individuality but also their collectivity i.e., their group affiliations. These group identities are the shared sense of belonging to a group. Online contact with others who share the same group identity can lead to cooperation and, even, coordination of social action initiatives both online and offline. Such social actions may be for the purposes of positive change, e.g., the Arab Spring in 2010, or disruptive, e.g., the England Riots in 2011. Stylometry and authorship attribution research has shown that it is possible to distinguish individuals based on their online language. In contrast, this work proposes and evaluates a model to analyse group identities online based on textual conversations amongst groups. We argue that textual features make it possible to automatically distinguish between different group identities and detect whether group identities are salient (i.e., most prominent) in the context of a particular conversation. We show that the salience of group identities can be detected with 95% accuracy and group identities can be distinguished from others with 84% accuracy. We also identify the most relevant features that may enable mal-actors to manipulate the actions of online groups. This has major implications for tools and techniques to drive positive social actions online or safeguard society from disruptive initiatives. At the same time, it poses privacy challenges given the potential ability to persuade or dissuade large groups online to move from rhetoric to action.

To be able to respond to source code modifications with large impact or commits that necessitate further examination, developers and managers in a software development team need to be aware of anything unusual happening in their software projects. To address this need, we introduce UEDashboard, a tool which automatically detects unusual events in a commit history based on metrics and smells, and surfaces them in an event feed. Our preliminary evaluation with a team of professional software developers showed that our conceptualization of unusual correlates with developers' perceptions of task difficulty, and that UEDashboard could be useful in supporting development meetings and for pre-commit warnings.

There is a growing interest of the Computer Science education community for including testing concepts on introductory programming courses. Aiming at contributing to this issue, we introduce POPT, a Problem-Oriented Programming and Testing approach for Introductory Programming Courses. POPT main goal is to improve the traditional method of teaching introductory programming that concentrates mainly on implementation and neglects testing. According to POPT, students' skills must be developed by dealing with ill-defined problems, from which students are stimulated to develop test cases in a table-like manner in order to enlighten the problems' requirements and also to improve the quality of generated code. This paper presents POPT and a case study performed in an Introductory Programming course of a Computer Science program at the Federal University of Rio Grande do Norte, Brazil. The study results have shown that, when compared to a Blind Testing approach, POPT stimulates the implementation of programs of better external quality - the first program version submitted by POPT students passed in twice the number of test cases (professor-defined ones) when compared to non-POPT students. Moreover, POPT students submitted fewer program versions and spent more time to submit the first version to the automatic evaluation system, which lead us to think that POPT students are stimulated to think better about the solution they are implementing.