Retrieving curated Stack Overflow Posts of similar project tasks

Software development depends on diverse technologies and methods and as a result, software development teams often handle issues in which team members are not experts. In order to address this lack of expertise, developers typically search for information on web-based Q&A sites such as Stack Overflow, a well-known place to find solutions to specific technology-related problems. Access to these web-based Q&A locations is currently not integrated into the software development environment, and since the associations between software development projects and the supporting sources of known solutions, usually referred to as knowledge, is not explicitly recorded, software developers often need to search for solutions to similar recurring issues multiple times. This lack of integration hinders the reuse of the knowledge obtained, besides not avoiding efforts of search and selection, curation, of this knowledge over and over again. This research aims at proposing a study regarding explicitly associating project elements (such as project tasks) to Stack Overflow posts that have already been curated by developers, and presents a study about Stack Overflow posts suggestions to developers based on similarity of project tasks.O desenvolvimento de software depende de diversas tecnologias e métodos e, como resultado, as equipes de desenvolvimento de software geralmente lidam com problemas em que não são especialistas. Para lidar com a falta de conhecimento, desenvolvedores normalmente procuram informações em sites de perguntas e respostas, como o Stack Overflow, um site usado para encontrar soluções para problemas específicos relacionados à tecnologia. O acesso a esses sites não é integrado ao ambiente de desenvolvimento de software e porque as associações entre os projetos de desenvolvimento de software e as fontes de suporte de soluções conhecidas não são explicitamente registradas. Com isso, desenvolvedores de software podem investir um esforço em procurar soluções para problemas semelhantes várias vezes. Essa falta de integração dificulta o reuso do conhecimento obtido, além de não evitar esforços de busca e seleção, a curadoria, repetidas vezes. Esta pesquisa tem como objetivo realizar um estudo sobre a associação explicita entre elementos do projeto (como tarefas de projeto) a publicações do Stack Overflow que já sofreram curadoria por desenvolvedores, e apresenta um estudo sobre sugestões de publicações do Stack Overflow a desenvolvedores com base na similaridade de tarefas de projeto

User Review-Based Change File Localization for Mobile Applications

In the current mobile app development, novel and emerging DevOps practices (e.g., Continuous Delivery, Integration, and user feedback analysis) and tools are becoming more widespread. For instance, the integration of user feedback (provided in the form of user reviews) in the software release cycle represents a valuable asset for the maintenance and evolution of mobile apps. To fully make use of these assets, it is highly desirable for developers to establish semantic links between the user reviews and the software artefacts to be changed (e.g., source code and documentation), and thus to localize the potential files to change for addressing the user feedback. In this paper, we propose RISING (Review Integration via claSsification, clusterIng, and linkiNG), an automated approach to support the continuous integration of user feedback via classification, clustering, and linking of user reviews. RISING leverages domain-specific constraint information and semi-supervised learning to group user reviews into multiple fine-grained clusters concerning similar users' requests. Then, by combining the textual information from both commit messages and source code, it automatically localizes potential change files to accommodate the users' requests. Our empirical studies demonstrate that the proposed approach outperforms the state-of-the-art baseline work in terms of clustering and localization accuracy, and thus produces more reliable results.Comment: 15 pages, 3 figures, 8 table

Translating Video Recordings of Mobile App Usages into Replayable Scenarios

Screen recordings of mobile applications are easy to obtain and capture a wealth of information pertinent to software developers (e.g., bugs or feature requests), making them a popular mechanism for crowdsourced app feedback. Thus, these videos are becoming a common artifact that developers must manage. In light of unique mobile development constraints, including swift release cycles and rapidly evolving platforms, automated techniques for analyzing all types of rich software artifacts provide benefit to mobile developers. Unfortunately, automatically analyzing screen recordings presents serious challenges, due to their graphical nature, compared to other types of (textual) artifacts. To address these challenges, this paper introduces V2S, a lightweight, automated approach for translating video recordings of Android app usages into replayable scenarios. V2S is based primarily on computer vision techniques and adapts recent solutions for object detection and image classification to detect and classify user actions captured in a video, and convert these into a replayable test scenario. We performed an extensive evaluation of V2S involving 175 videos depicting 3,534 GUI-based actions collected from users exercising features and reproducing bugs from over 80 popular Android apps. Our results illustrate that V2S can accurately replay scenarios from screen recordings, and is capable of reproducing $\approx$ 89% of our collected videos with minimal overhead. A case study with three industrial partners illustrates the potential usefulness of V2S from the viewpoint of developers.Comment: In proceedings of the 42nd International Conference on Software Engineering (ICSE'20), 13 page