Online programming discussion platforms such as Stack Overflow serve as a rich source of information for software developers. Available information include vibrant discussions and oftentimes ready-to-use code snippets. Anecdotes report that software developers copy and paste code snippets from those information sources for convenience reasons. Such behavior results in a constant flow of community-provided code snippets into production software. To date, the impact of this behaviour on code security is unknown. We answer this highly important question by quantifying the proliferation of security-related code snippets from Stack Overflow in Android applications available on Google Play. Access to the rich source of information available on Stack Overflow including ready-to-use code snippets provides huge benefits for software developers. However, when it comes to code security there are some caveats to bear in mind: Due to the complex nature of code security, it is very difficult to provide ready-to-use and secure solutions for every problem. Hence, integrating a security-related code snippet from Stack Overflow into production software requires caution and expertise. Unsurprisingly, we observed insecure code snippets being copied into Android applications millions of users install from Google Play every day. To quantitatively evaluate the extent of this observation, we scanned Stack Overflow for code snippets and evaluated their security score using a stochastic gradient descent classifier. In order to identify code reuse in Android applications, we applied state-of-the-art static analysis. Our results are alarming: 15.4 of the 1.3 million Android applications we analyzed, contained security-related code snippets from Stack Overflow. Out of these 97.9 contain at least one insecure code snippet.
@cite_40 report that developer discussion platforms like are very effective at code reviews and conceptual questions. @cite_48 investigate the interplay of activity and development process on GitHub. They conclude that knowledge of the GitHub community flows into . In turn, this knowledge increases the number of commits of users on GitHub. @cite_27 created an algorithm to link questions with Android classes detected in source code. They found that Android developer question counts peak on immediately after APIs receive updates that modify their behavior.
@cite_48 @cite_40 @cite_27
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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. Online Question and Answer websites for developers have emerged as the main forums for interaction during the software development process. The veracity of an answer in such websites is typically verified by the number of 'upvotes' that the answer garners from peer programmers using the same forum. Although this mechanism has proved to be extremely successful in rating the usefulness of the answers, it does not lend itself very elegantly to model the expertise of a user in a particular domain. In this paper, we propose a model to rank the expertise of the developers in a target domain by mining their activity in different opensource projects. To demonstrate the validity of the model, we built a recommendation system for StackOverflow which uses the data mined from GitHub. Code comments improve software maintainability. To address the comment scarcity issue, we propose a new automatic comment generation approach, which mines comments from a large programming Question and Answer (Q&A) site. Q&A sites allow programmers to post questions and receive solutions, which contain code segments together with their descriptions, referred to as code-description mappings. We develop AutoComment to extract such mappings, and leverage them to generate description comments automatically for similar code segments matched in open-source projects. We apply AutoComment to analyze Java and Android tagged Q&A posts to extract 132,767 code-description mappings, which help AutoComment to generate 102 comments automatically for 23 Java and Android projects. The user study results show that the majority of the participants consider the generated comments accurate, adequate, concise, and useful in helping them understand the code. As Android platform becomes more and more popular, a large amount of Android applications have been developed. When developers design and implement Android applications, power consumption management is an important factor to consider since it affects the usability of the applications. Thus, it is important to help developers adopt proper strategies to manage power consumption. Interestingly, today, there is a large number of Android application repositories made publicly available in sites such as GitHub. These repositories can be mined to help crystalize common power management activities that developers do. These in turn can be used to help other developers to perform similar tasks to improve their own Android applications.In this paper, we present an empirical study of power management commits in Android applications. Our study extends that of who perform an empirical studyon energy aware commits; however they do not focus on Android applications and only a few of the commits that they study come from Android applications. Android applications are often different from other applications (e.g., those running on a server) due to the issue of limited battery life and the use of specialized APIs. As subjects of our empirical study, we obtain a list of open source Android applications from F-Droid and crawl their commits from Github. We get 468 power management commits after we filter the commits using a set of keywords and by performing manual analysis. These 468 power management commits are from 154 different Android applications and belong to 15 different application categories. Furthermore, we use open card sort to categorize these power management commits and we obtain 6 groups which correspond to different power management activities. Our study also reveals that for different kinds of Android application (e.g., Games, Connectivity, Navigation, etc.), the dominant power management activities differ.For example, the percentageof power management commits belonging to Power Adaptation activity is larger for Navigation applications than those belonging to other categories.