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Mobile applications demand is on the rise, leading to more programmers learning to develop or having to maintain this kind of programs. Developers often refer to online resources to find inspiration or answers to questions they have about mobile programming topics and screencasts are a popular resource. However, given the multitude of screencasts available, it can be difficult to quickly comprehend which of the many videos is relevant to one's needs. We propose a novel approach, called UIScreens, which detects, extracts, and presents the most representative user interface (UI) screens embedded in mobile development screencasts. This could help developers quickly comprehend what an app displayed in a video is about, therefore saving time searching for useful videos. UIScreens has been evaluated in two empirical studies on iOS and Android programming screencasts. The first study investigates the accuracy of our UI extraction and shows that our approach is able to detect and extract UI screens with an accuracy of 94%. The second is a user study with mobile app developers, who evaluated both the accuracy and the usefulness of UIScreens. They agreed that UIScreens is accurate and extracts representative UI screens from videos. They considered that the extracted UI screens are useful for understanding what a video is about and if it is relevant to a search. Our approach has been implemented as a free online tool. 

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. 

Programming screencasts can be a rich source of documentation for developers. However, despite the availability of such videos, the information available in them, and especially the source code being displayed is not easy to find, search, or reuse by programmers. Recent work has identified this challenge and proposed solutions that identify and extract source code from video tutorials in order to make it readily available to developers or other tools. A crucial component in these approaches is the Optical Character Recognition (OCR) engine used to transcribe the source code shown on screen. Previous work has simply chosen one OCR engine, without consideration for its accuracy or that of other engines on source code recognition. In this paper, we present an empirical study on the accuracy of six OCR engines for the extraction of source code from screencasts and code images. Our results show that the transcription accuracy varies greatly from one OCR engine to another and that the most widely chosen OCR engine in previous studies is by far not the best choice. We also show how other factors, such as font type and size can impact the results of some of the engines. We conclude by offering guidelines for programming screencast creators on which fonts to use to enable a better OCR recognition of their source code, as well as advice on OCR choice for researchers aiming to analyze source code in screencasts. 

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. 

The need for mobile applications and mobile programming is increasing due to the continuous rise in the pervasiveness of mobile devices. Developers often refer to video programming tutorials to learn more about mobile programming topics. To find the right video to watch, developers typically skim over several videos, looking at their title, description, and video content in order to determine if they are relevant to their information needs. Unfortunately, the title and description do not always provide an accurate overview, and skimming over videos is time-consuming and can lead to missing important information. We propose a novel approach that locates and extracts the GUI screens showcased in a video tutorial, then selects and displays the most representative ones to provide a GUI-focused overview of the video. We believe this overview can be used by developers as an additional source of information for determining if a video contains the information they need. To evaluate our approach, we performed an empirical study on iOS and Android programming screencasts which investigates the accuracy of our automated GUI extraction. The results reveal that our approach can detect and extract GUI screens with an accuracy of 94%.