Search

Pull requests are a key part of the collaborative software development and code review process today. However, pull requests can also slow down the software development process when the reviewer(s) or the author do not actively engage with the pull request. In this work, we design an end-to-end service, Nudge, for accelerating overdue pull requests toward completion by reminding the author or the reviewer(s) to engage with their overdue pull requests. First, we use models based on effort estimation and machine learning to predict the completion time for a given pull request. Second, we use activity detection to filter out pull requests that may be overdue but for which sufficient action is taking place nonetheless. Last, we use actor identification to understand who the blocker of the pull request is and nudge the appropriate actor (author or reviewer(s)). The key novelty of Nudge is that it succeeds in reducing pull request resolution time, while ensuring that developers perceive the notifications sent as useful, at the scale of thousands of repositories. In a randomized trial on 147 repositories in use at Microsoft, Nudge was able to reduce pull request resolution time by 60% for 8,500 pull requests, when compared to overdue pull requests for which Nudge did not send a notification. Furthermore, developers receiving Nudge notifications resolved 73% of these notifications as positive. We observed similar results when scaling up the deployment of Nudge to 8,000 repositories at Microsoft, for which Nudge sent 210,000 notifications during a full year. This demonstrates Nudge’s ability to scale to thousands of repositories. Last, our qualitative analysis of a selection of Nudge notifications indicates areas for future research, such as taking dependencies among pull requests and developer availability into account. [ABSTRACT FROM AUTHOR]

Using JAVASCRIPT and dynamic DOM manipulation on the client side of Web applications is becoming a widespread approach for achieving rich interactivity and responsiveness in modern Web applications. At the same time, such techniques--collectively known as AJAX--shatter the concept of webpages with unique URLs, on which traditional Web crawlers are based. This article describes a novel technique for crawling AJAX-based applications through automatic dynamic analysis of user-interface-state changes in Web browsers. Our algorithm scans the DOM tree, spots candidate elements that are capable of changing the state, fires events on those candidate elements, and incrementally infers a state machine that models the various navigational paths and states within an AJAX application. This inferred model can be used in program comprehension and in analysis and testing of dynamic Web states, for instance, or for generating a static version of the application. In this article, we discuss our sequential and concurrent AJAX crawling algorithms. We present our open source tool called CRAWLJAX, which implements the concepts and algorithms discussed in this article. Additionally, we report a number of empirical studies in which we apply our approach to a number of open-source and industrialWeb applications and elaborate on the obtained results. [ABSTRACT FROM AUTHOR]

Program comprehension is an important activity in software maintenance, as software must be sufficiently understood before it can be properly modified. The study of a program's execution, known as dynamic analysis, has become a common technique in this respect and has received substantial attention from the research community, particularly over the last decade. These efforts have resulted in a large research body of which currently there exists no comprehensive overview. This paper reports on a systematic literature survey aimed at the identification and structuring of research on program comprehension through dynamic analysis. From a research body consisting of 4,795 articles published in 14 relevant venues between July 1999 and June 2008 and the references therein, we have systematically selected 176 articles and characterized them in terms of four main facets: activity, target, method, and evaluation. The resulting overview offers insight in what constitutes the main contributions of the field, supports the task of identifying gaps and opportunities, and has motivated our discussion of several important research directions that merit additional consideration in the near future. [ABSTRACT FROM AUTHOR]