ABSTRACT Context Developers often turn to readme files in GitHub repositories when they intend to contribute, reuse, or extend a project. These files act as a primary source of information, offering insights into various aspects of the repository. The content and organization of readme can have a significant impact on the project's popularity, its development progress, and the growth of its community. Objective We examine the growth of GitHub repositories through the lens of their meta‐characteristics, which encompass factors like popularity, community engagement, and development progress, to study the relationship of readme files with repository growth. To achieve this, we conduct a correlation analysis to assess the relationship between project meta‐characteristics and the content and organization of a readme file. Methods To conduct a correlation analysis between readme files and project meta‐characteristics, we compiled a data set of readme files from 2000 public GitHub repositories, encompassing 10 primary programming languages. We define metrics for popularity, progress, and community engagement meta‐characteristics and analyze the correlation of structural features and categorical content in readme files against these metrics using non‐parametric statistical tests. Results The results are presented from three distinct perspectives of project meta‐characteristics across 10 programming languages. The results reveal a positive and strong association of external reference links, contribution guidelines, and team details in readme files with better popularity and larger community size. However, the influence of contribution guidelines in readme files on rate of progress is not clearly evident. Conclusion The study was conducted to assess the correlation of readme file content on repository popularity, development progress, and community size. Our experiments revealed positive correlations between readme content and project meta‐characteristics. However, the extent of correlation varied between repositories across programming languages and meta‐characteristics. We discussed the implications of our findings for developers and researchers and have proposed recommendations for repository owners to organize readme files toward increasing the growth of the repositories with better meta‐characteristics.

Readme files and commit logs carry important and useful project information, corresponding to project dependencies, project functionalities, additions, deletions, and so on. These two artifacts have been analysed separately to obtain project specific information corresponding to contribution guidelines, bug prediction and localisation. Linking the readme files with associated commits and further querying the linked data could help in assessing time stamp specific changes made to the readme files. Utilizing knowledge graph representation of data is observed to largely support querying and integration and extraction of data from heterogeneous sources. To this end, we present a tool to generate readme specific temporal knowledge graph, as a first step towards integrating readme files and commit logs. As commits contain temporal information of the changes, we see that overlaying this information over the corresponding text in readme files could help in arriving at a temporal knowledge graph (TKG). We present a case study of querying the TKG for one repository and further evaluate the tool on 10 repositories spanning across 10 programming languages on GitHub. For demo video, visit - https://youtu.be/4YOCDngf4bY. For website of the tool, visit - https://akhilasrimanasa.github.io/rcgraph/

Software documentation is one of the critical aspects of a software project, that could support multiple tasks throughout the software development life-cycle. There is extensive research on understanding issues and challenges with existing documentation, which is typically available as readme files. In projects that support collaborative development, such as those on GitHub, other software artifacts such as commits, pull requests and issues, apart from the conventional readme files, wikis and source code comments, also contain useful information, that supports in understanding, using, extending and maintaining the project. However, we are not aware of any dataset that explicitly focuses on documentation-related information in multiple software artifacts such as readme files, commits and pull requests across a repository. To address this concern and to facilitate further research in software documentation, we present DocMine, as a dataset of documentation-related information, extracted from around 1.35M software artifacts in 950 GitHub repositories, spanning across four different programming languages. The dataset along with its documentation is made available in CSV and .sql formats at - https://doi.org/10.5281/zenodo.5195084.

GitHub hosts millions of software repositories, facilitating developers to contribute to many projects in multiple ways. Most of the information about the repositories is text-based in the form of stars, forks, commits, and so on. However, developers willing to contribute to projects on GitHub often find it challenging to select appropriate projects to contribute to or reuse due to the large number of repositories present on GitHub. Further, obtaining this required information often becomes a tedious process, as one has to carefully mine information hidden inside the repository. To alleviate the effort intensive mining procedures, researchers have proposed npm-badges to outline information relating to build status of a project. However, these badges are static and limit their usage to package dependency and build details. Adding visual cues such as badges, (see PDF) to the repositories might reduce the search space for developers. Hence, we present GitQ, to automatically augment GitHub repositories with badges representing information about source code and project maintenance. Presenting GitQ as a browser plugin to GitHub could make it easily accessible to developers using GitHub. GitQ is evaluated with 15 developers based on the UTAUT model to understand developer perception towards its usefulness. We observed that 11 out of 15 developers perceived GitQ to be useful in identifying the right set of repositories using visual cues such as (see PDF) generated by GitQ. The source code and tool are available for download on GitHub at https://github.com/gitq-for-github/plugin, and the demo can be found at https://youtu.be/c0yohmIat3A.

Covid-19 has been causing severe loss to the human race. Considering the mode of spread and severity, it is essential to make it a habit to follow various safety precautions such as using sanitizers and masks and maintaining social distancing to prevent the spread of Covid-19. Individuals are widely educated about the safety measures against the disease through various modes such as announcements through online or physical awareness campaigns, advertisements in the media, and so on. The younger generations today spend considerably more time on mobile phones and games. However, there are very few applications or games aimed to help in practicing safety measures against a pandemic, which is much lesser in the case of Covid-19. Hence, we propose a 2D survival-based game, SurviveCovid-19, aimed to educate people about safety precautions to be taken for Covid-19 outside their homes by incorporating social distancing and usage of masks and sanitizers in the game. SurviveCovid-19 has been designed as an Android-based mobile game, along with a desktop (browser) version and has been evaluated through a remote quantitative user survey, with 30 volunteers using the questionnaire based on the MEEGA+ model. The survey results are promising, with all the survey questions having a mean value greater than 3.5. The game’s quality factor was 69.3, indicating that the game could be classified as excellent quality, according to the MEEGA+ model.

Game engines, are frameworks that provide a platform for developers to build games with an interface tailored to handle the complexity of game development. Though there is extensive empirical research on software frameworks, there is a need for empirical studies on game engines, as they differ from traditional software frameworks. Thus, to aid research and development in the area of game engines, we present GE526, a curated dataset of 526 game engine repositories mined from GitHub, which can help researchers to analyze game engines in terms of the release cycles, code quality, API usability and so on. To the best of our knowledge, we are not aware of any curated dataset that caters to game engines in the literature. The dataset contains metadata of all the mined repositories, including 582,079 commits, 20,138 pull requests, 30,287 issues reports and 2,111 releases. The dataset along with its documentation is made available at - https://bit.ly/3pyexnc.

Many novice programmers visit Stack Overflow for purposes that include posing questions and finding answers for issues they come across in the process of programming. Many questions have more than one correct answer on Stack Overflow, which are accompanied by number of comments from the users. Comments help developers in identifying the answer that better fits their purpose. However, it is difficult to navigate through all the comments to select an answer. Adding relevant visual cues to comments could help developers in prioritizing the comments to be read. Comments logged generally include sentiments of users, which, when depicted visually, could motivate users in reading through the comments and also help them in prioritizing the comments. However, the sentiment of comments is not being explicitly depicted on the current Stack Overflow platform. While there exist many tools that augment or annotate Stack Overflow platform for developers, we are not aware of tools that annotate visual representations of sentiments to the posts. In this paper, we propose StackEmo as a Google Chrome plugin to augment comments on Stack Overflow with emojis, based on the sentiment of the comments posted. We evaluated StackEmo through an in-user likert scale based survey with 30 university students to understand user perception towards StackEmo. The results of the survey provided us insights on improving StackEmo, with 83% of the participants willing to recommend the plugin to their peers. The source code and tool are available for download on GitHub at: https://github.com/rishalab/StackEmo, and the demo can be found here on youtube: https://youtu.be/BCFlqvMhTMA.

Abstract Extensive development of games for various purposes including education and entertainment has resulted in increased development of game engines. Game engines are being used on a large scale as they support and simplify game development to a greater extent. Game developers using game engines are often compelled to use various application programming interfaces (APIs) of game engines in the process of game development. Thus, both quality and ease of development of games are greatly influenced by APIs defined in game engines. Hence, understanding API usability in game engines could greatly help in choosing better game engines among the ones that are available for game development and also could help developers in designing better game engines. In this article, we thus aim to evaluate API usability of 95 publicly available game engine repositories on GitHub, written primarily in C++ programming language. We test API usability of these game engines against the eight structural API usability metrics—AMNOI, AMNCI, AMGI, APXI, APLCI, AESI, ATSI, and ADI. We see this research as a first step toward the direction of improving usability of APIs in game engines. We present the results of the study, which indicate that about 25% of the game engines considered have minimal API usability, with respect to the considered metrics. It was observed that none of the considered repositories have ideal (all metric scores equal to 1) API usability, indicating the need for developers to consider API usability metrics while designing game engines.

There is a significant rise in the availability, development and size of software projects in the present day. Many open source projects are reused or updated for various purposes that include fixing bugs in existing projects, development and maintenance of project extensions. Developers who interact with the projects might require documentation for better comprehension of the project and to develop extensions. Most of the software projects currently do not have sufficient documentation or it is not updated along with the project. If some projects have reasonably sufficient documentation, it is usually difficult to comprehend it either for maintenance or for reuse purposes. Considering the usefulness of Augmented Reality (AR) towards comprehension, we propose the vision of integrating the domains of augmented reality and software documentation, and specifically, visualization of software documentation using AR. In this paper, we present some of the directions that could be explored towards this vision and also present an example visualization scenario for API documentation using neural system metaphor. We see this paper as a basis for the future research direction of leveraging AR towards making documentation as a primary artifact in the software development process.

Museums act as a vehicle to collect, preserve and demonstrate historical, cultural and scientific heritage to a larger community of end users. However, there is neither increase in the number of physical visitors nor visitors to online museum, despite their availability on the web. On the other hand, Augmented Reality has emerged as a potential technology to support and enhance experience of end users in different communities especially in digital heritage. In this paper, we propose Augmented Reality Museum (ARM) as an application that can enhance online museum visitor experience. Augmented Reality can be integrated as a mobile application that provides 3D view of an artifact, along with information about its historic, artistic and/or scientific importance. We present the design and development of ARM and demonstrate it for the case study of Online British Museum. Our user experience study conducted with 21 volunteers shows that ARM is potentially a good way to enhance user experience.

There is a tremendous increase in the use of online coding platforms, courses and walkthrough tutorials to learn programming today. Stack Overflow, a Q&A website of crowd-sourced knowledge on programming is one of the popular platforms that developers and learners use to ask and answer Q&As related to programming. However, novice programmers often face difficulties in understanding the answers as they may contain new terminologies, function calls and attributes of certain technology or programming language. Researchers have proposed different ways to augment Stack Overflow in the literature, but to the best of our knowledge, there is no work that exists to augment Stack Overflow posts with definitions of API calls and relevant examples. To this end, we propose StackDoc, a prototype plug-in that augments Stack Overflow with definitions and examples of API calls in the questions and answers with the goal of helping novice programmers. We did a preliminary survey with 20 students of various universities, novice to Java and 85% of the users reported positive experience with the plugin.

There is an ever increasing growth in the use of Q&A websites such as Stack Overflow (SO), so are the number of posts on them.These websites serve as knowledge sharing platforms where Subject Matter Experts (SMEs) and developers answer questions posted by other users.It is effort intensive for developers to navigate to right posts because of the large volume of posts on the platform, despite the presence of existing tags, that are based on technologies.Tagging these posts based on their context and purpose might help developers and SMEs in easily identifying questions they wish to answer and also in identifying contextually similar posts.To support this idea, we propose SOTagger as a prototype plug-in for Stack Overflow to tag questions contextually.We have considered SO data provided on SOTorrent and automated the identification of 6 categories of questions using Latent Dirichlet Allocation.We have also manually verified relevance of these categories.Using these categories and dataset, we have built a classification model to classify a post into one of these six categories using Support Vector Machine.We have evaluated SOTagger by conducting a user survey with 32 developers.The preliminary results are promising with about 80% developers recommending the plugin to others.