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.

Comprehending arrays in programming is considered as one of the difficult tasks for novice programmers because of the complexity involved in their definition, declaration, and operations. Visualization of data structures and algorithms is a valuable learning aid for novice programmers. However, existing techniques show the visualization of code after compiling the program, rather than during the development process. To address this, we present SupportArr, a tool that helps in the visualization of arrays in C/C++ programming languages. The core idea is to provide an in-built IDE support for novice programmers to visualize the statements in code without executing the program. We conducted a preliminary experiment with 23 novice programmers and got a promising result from 84% of the participants.

Language Learning can lead to the development of one's communication abilities and helps individuals to express, read, write, and think. Many tools and technologies have been proposed to make language learning an easy and enjoyable process. Games are one of the ways that aid in an informative, exciting, and fun learning experience. Thus, we propose L2to help novice language learners to grasp the Indian language vocabulary based on the clues of object images. L2is a 3D game where the player is provided with a cloud of letters and an image of object to interpret. Observing and understanding the image, the player chooses a set of letters that would form a word corresponding to it. On choosing the right set of letters, the player gets promoted to the next level; otherwise, the player is asked to restart the level. We implemented L2for two Indian languages, namely Hindi and Telugu, using Unity 3D. We evaluated this game with 22 participants and got a favourable response from 89% of the users.

Augmented Reality (AR) is one of the emerging technologies which helps in enhancing user experience by providing a composite view of the real-world objects. It supports the user with superimposed information on top of the object, thus creating a live experience. Besides, coding is pervasive in today's world, and there is a strong need for well-equipped developers with thorough theoretical and practical knowledge. Data Structures and Abstract Syntax Trees are two of the fundamental topics of the computer science domain. Data structures help in the organization of data, whereas, Abstract Syntax Trees (ASTs) enable better comprehension of source code by syntactically analyzing it. In this paper, we propose an augmented reality-based software application AST[AR] to teach data structures with the help of ASTs. We conducted a user experience study with 30 volunteers and got positive feedback from 80.6% of the participants. We believe that this software application will help in bifold learning of data structures as well as ASTs with exciting and enjoyable 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.