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Mobile apps are one of the most widely used types of software systems in existence today and more programmers and students learn how to develop them everyday. One of the most popular resources for learning mobile programming are videos hosted on social platforms such as YouTube. While useful, this type of resource has also its limitations, especially when developers are looking for user interface (UI) designs for mobile applications, since these are hard to search for and locate in videos. We propose UIScreens, a web-based analysis and search engine that analyzes the visual contents of mobile programming video tutorials, then identifies and extracts the UI screens displayed in the videos. Our tool offers features such as searching for UI screens in videos, displaying an overview of the UI screens identified in a video under each search result, and navigating to the part of a video where a particular UI screen is being displayed and discussed. In a user study, participants agreed that UIScreens is usable and useful to quickly skim through videos, while the UI screens it extracts can help developers further determine the relevance of videos to a search topic. 

Team communication is essential for the development of modern software systems. For distributed software development teams, such as those found in many open source projects, this communication usually takes place using electronic tools. Among these, modern chat platforms such as Gitter are becoming the de facto choice for many software projects due to their advanced features geared towards software development and effective team communication. Gitter channels contain numerous messages exchanged by developers regarding the state of the project, issues and features of the system, team logistics, etc. These messages can contain important information to researchers studying open source software systems, developers new to a particular project and trying to get familiar with the software, etc. Therefore, uncovering what developers are communicating about through Gitter is an essential first step towards successfully understanding and leveraging this information. We present a new dataset, called GitterCom, which aims to enable research in this direction and represents the largest manually labeled and curated dataset of Gitter developer messages. The dataset is comprised of 10,000 messages collected from 10 Gitter communities associated with the development of open source software. Each message was manually annotated and verified by two of the authors, capturing the purpose of the communication expressed by the message. While the dataset has not yet been used in any publication, we discuss how it can enable interesting research opportunities. 

Software developers use modern chat platforms to communicate about the status of a project and to coordinate development and release efforts, among other things. Developers also use chat platforms to ask technical questions to other developers. While some questions are project-specific and require an experienced developer familiar with the system to answer, many questions are rather general and may have been already answered by other developers on platforms such as the Q&A site StackOverflow. In this paper, we present GitterAns, a bot that can automatically detect when a developer asks a technical question in a chat and leverages the information present in Q&A forums to provide the developer with possible answers to their question. The results of a preliminary study indicate promising results, with GitterAns achieving an accuracy of 0.78 in identifying technical questions. 

Abstract Like many highly variable human traits, more than a dozen genes are known to contribute to the full range of skin color. However, the historical bias in favor of genetic studies in European and European‐derived populations has blinded us to the magnitude of pigmentation's complexity. As deliberate efforts are being made to better characterize diverse global populations and new sequencing technologies, better measurement tools, functional assessments, predictive modeling, and ancient DNA analyses become more widely accessible, we are beginning to appreciate how limited our understanding of the genetic bases of human skin color have been. Novel variants in genes not previously linked to pigmentation have been identified and evidence is mounting that there are hundreds more variants yet to be found. Even for genes that have been exhaustively characterized in European populations like MC1R, OCA2, and SLC24A5, research in previously understudied groups is leading to a new appreciation of the degree to which genetic diversity, epistatic interactions, pleiotropy, admixture, global and local adaptation, and cultural practices operate in population‐specific ways to shape the genetic architecture of skin color. Furthermore, we are coming to terms with how factors like tanning response and barrier function may also have influenced selection on skin throughout human history. By examining how our knowledge of pigmentation genetics has shifted in the last decade, we can better appreciate how far we have come in understanding human diversity and the still long road ahead for understanding many complex human traits.