Increasingly, software developers are using a wide array of social collaborative platforms for software development and learning. In this work, we examined the similarities in developer's interests within and across GitHub and Stack Overflow. Our study finds that developers share common interests in GitHub and Stack Overflow, on average, 39 of the GitHub repositories and Stack Overflow questions that a developer had participated fall in the common interests. Also, developers do share similar interests with other developers who co-participated activities in the two platforms. In particular, developers who co-commit and co-pull-request same GitHub repositories and co-answer same Stack Overflow questions, share more common interests compare to other developers who co-participate in other platform activities.
Stack Overflow and GitHub have also been studied for empirical works on developer interests. For example, there were research works that focused on analyzing topics asked by developers in Stack Overflow @cite_1 @cite_5 @cite_8 @cite_4 @cite_15 @cite_16 . Similarly, there were also works on analyzing programming languages used by developers in GitHub and their relationships to GitHub contributions @cite_7 @cite_10 @cite_0 @cite_9 @cite_14 @cite_13 @cite_12 . There are also studies characterizing social network properties of GitHub and Stack Overflow @cite_3 @cite_16 . Our work extends this group of research by comparing developer interests in the two social collaborative platforms. To our best of knowledge, our work is the first inter-network study that examines cross-site developer interests in GitHub and Stack Overflow.
@cite_13 @cite_14 @cite_4 @cite_7 @cite_8 @cite_9 @cite_1 @cite_3 @cite_0 @cite_5 @cite_15 @cite_16 @cite_10 @cite_12
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Given the increasing number of unsuccessful pull requests in GitHub projects, insights into the success and failure of these requests are essential for the developers. In this paper, we provide a comparative study between successful and unsuccessful pull requests made to 78 GitHub base projects by 20,142 developers from 103,192 forked projects. In the study, we analyze pull request discussion texts, project specific information (e.g., domain, maturity), and developer specific information (e.g., experience) in order to report useful insights, and use them to contrast between successful and unsuccessful pull requests. We believe our study will help developers overcome the issues with pull requests in GitHub, and project administrators with informed decision making. The popularity of mobile devices has been steadily growing in recent years. These devices heavily depend on software from the underlying operating systems to the applications they run. Prior research showed that mobile software is different than traditional, large software systems. However, to date most of our research has been conducted on traditional software systems. Very little work has focused on the issues that mobile developers face. Therefore, in this paper, we use data from the popular online Q&A site, Stack Overflow, and analyze 13,232,821 posts to examine what mobile developers ask about. We employ Latent Dirichlet allocation-based topic models to help us summarize the mobile-related questions. Our findings show that developers are asking about app distribution, mobile APIs, data management, sensors and context, mobile tools, and user interface development. We also determine what popular mobile-related issues are the most difficult, explore platform specific issues, and investigate the types (e.g., what, how, or why) of questions mobile developers ask. Our findings help highlight the challenges facing mobile developers that require more attention from the software engineering research and development communities in the future and establish a novel approach for analyzing questions asked on Q&A forums. Feedback from software users constitutes a vital part in the evolution of software projects. By filing issue reports, users help identify and fix bugs, document software code, and enhance the software via feature requests. Many studies have explored issue reports, proposed approaches to enable the submission of higher-quality reports, and presented techniques to sort, categorize and leverage issues for software engineering needs. Who, however, cares about filing issues? What kind of issues are reported in issue trackers? What kind of correlation exist between issue reporting and the success of software projects? In this study, we address the need for answering such questions by performing an empirical study on a hundred thousands of open source projects. After filtering relevant trackers, the study used about 20,000 projects. We investigate and answer various research questions on the popularity and impact of issue trackers. Programming question and answer (QA questions in some topics lead to discussions in other topics; and the topics gaining the most popularity over time are web development (especially jQuery), mobile applications (especially Android), Git, and MySQL. Social coding enables a different experience of software development as the activities and interests of one developer are easily advertised to other developers. Developers can thus track the activities relevant to various projects in one umbrella site. Such a major change in collaborative software development makes an investigation of networkings on social coding sites valuable. Furthermore, project hosting platforms promoting this development paradigm have been thriving, among which GitHub has arguably gained the most momentum. In this paper, we contribute to the body of knowledge on social coding by investigating the network structure of social coding in GitHub. We collect 100,000 projects and 30,000 developers from GitHub, construct developer-developer and project-project relationship graphs, and compute various characteristics of the graphs. We then identify influential developers and projects on this sub network of GitHub by using PageRank. Understanding how developers and projects are actually related to each other on a social coding site is the first step towards building tool supports to aid social programmers in performing their tasks more efficiently. Users on GitHub can watch repositories to receive notifications about project activity. This introduces a new type of passive project membership. In this paper, we investigate the behavior of watchers and their contribution to the projects they watch. We find that a subset of project watchers begin contributing to the project and those contributors account for a significant percentage of contributors on the project. As contributors, watchers are more confident and contribute over a longer period of time in a more varied way than other contributors. This is likely attributable to the knowledge gained through project notifications. Modern web applications consist of a significant amount of client- side code, written in JavaScript, HTML, and CSS. In this paper, we present a study of common challenges and misconceptions among web developers, by mining related questions asked on Stack Over- flow. We use unsupervised learning to categorize the mined questions and define a ranking algorithm to rank all the Stack Overflow questions based on their importance. We analyze the top 50 questions qualitatively. The results indicate that (1) the overall share of web development related discussions is increasing among developers, (2) browser related discussions are prevalent; however, this share is decreasing with time, (3) form validation and other DOM related discussions have been discussed consistently over time, (4) web related discussions are becoming more prevalent in mobile development, and (5) developers face implementation issues with new HTML5 features such as Canvas. We examine the implications of the results on the development, research, and standardization communities. StackOverflow provides a popular platform where developers post and answer questions. Recently, manually label 385 questions in StackOverflow and group them into 10 categories based on their contents. They also analyze how tags are used in StackOverflow. In this study, we extend their work to obtain a deeper understanding on how developers interact with one another on such a question and answer web site. First, we analyze the distributions of developers who ask and answer questions. We also investigate if there is a segregation of the StackOverflow community into questioners and answerers. We also perform automated text mining to find the various kinds of topics asked by developers. We use Latent Dirichlet Allocation (LDA), a well known topic modeling approach, to analyze the contents of tens of thousands of questions and answers, and produce five topics. Our topic modeling strategy provides an alternative perspective different from that of for categorizing StackOverflow questions. Each question can now be categorized into several topics with different probabilities, and the learned topic model could automatically assign a new question to several categories with varying probabilities. Last but not least, we show the distributions of questions and developers belonging to various topics generated by LDA.