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.

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Pointers are considered as one of the key concepts in learning programming and are extensively used for implementing several data structures. They lay the foundation for handling dynamic aspects of a program, increase execution speed and handle data types with more efficiency. This makes it critical for budding programmers to be well versed with using pointers. However, most of the novice programmers find it difficult and tricky to understand concepts such as address allocations, pointers referring pointers and data structures containing pointers. Hence, drawing the physical structure and flow of pointers is considered to be a common learning practice to gain better clarity and avoid confusion when learning pointers. But, it is time consuming and tedious to draw the flow of pointers on paper while programming. To help programmers understand these variations in pointers, we propose PointerViz as a Google Chrome extension that displays the pictorial representation of selected code with pointers. We conducted a preliminary survey with 40 students from various universities and 83% of the users reported positive experience with the plugin.

Game engines provide a platform for developers to build games with an interface tailored to handle the complexity during game development. To reduce effort and improve quality of game development, there is a strong need to understand and analyze the quality of game engines and their various aspects such as API usability, code quality, code reuse and so on. To the best our knowledge, we are not aware of any dataset that caters to game engines in the literature. To this end, we present GE852, a dataset of 852 game engine repositories mined from GitHub in two languages, namely Java and C++. The dataset contains metadata of all the mined repositories including commits, pull requests, issues and so on. We believe that our dataset can lay foundation for empirical investigation in the area of game engines.