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Studying Trends, Topics, and Duplicate Questions on Q&A Websites for Game Developers

  • Author / Creator
    Kamienski, Arthur Veloso
  • The game development industry is growing and there is a high demand for developers that can produce high-quality games. These developers need resources to learn and improve the skills required to build those games in a reliable and easy manner. Question and Answer (Q&A) websites are learning resources that are commonly used by software developers to share knowledge and acquire the information they need. However, we still know little about how game developers use and interact with Q&A websites. In this thesis, we analyze the largest Q&A websites that discuss game development to understand how effective they are as learning resources and what can be improved to build a better Q&A community for their users. In the first part of this thesis, we analyzed data collected from four Q&A websites, namely Unity Answers, the Unreal Engine 4 (UE4) AnswerHub, the Game Development Stack Exchange, and Stack Overflow, to assess their effectiveness in helping game developers. We also used the 347 responses collected from a survey we ran with game developers to gauge their perception of Q&A websites. We found that the studied websites are in decline, with their activity and effectiveness decreasing over the last few years and users having an overall negative view of the studied Q&A communities. We also characterized the topics discussed in those websites using a latent Dirichlet allocation (LDA) model, and analyze how those topics differ across websites. Finally, we give recommendations to guide developers to the websites that are most effective in answering the types of questions they have, which could help the websites in overcoming their decline. In the second part of the thesis, we explored how we can further help Q&A websites for game developers by automatically identifying duplicate questions. Duplicate questions have a negative impact on Q&A websites by overloading them with questions that have already been answered. Therefore, we analyzed the performance of seven unsupervised and pre-trained techniques on the task of detecting duplicate questions on Q&A websites for game developers. We achieved the highest performance when comparing all the text content of questions and their answers using a pre-trained technique based on MPNet. Furthermore, we could almost double the performance by combining all of the techniques into a single question similarity score using supervised models. Lastly, we show that the supervised models can be used on websites different from the ones they were trained on with little to no decrease in performance. Our findings can be used by Q&A websites and future researchers to build better systems for duplicate question detection, which can ultimately provide game developers with better Q&A communities.

  • Subjects / Keywords
  • Graduation date
    Fall 2021
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/r3-ff7z-y736
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.