( = Paper PDF, = Presentation slides, = Presentation video)
1.
Arthur V. Kamienski
Studying Trends, Topics, and Duplicate Questions on Q&A Websites for Game Developers Masters Thesis
University of Alberta, 2021.
Abstract | BibTeX | Tags: Computer games, Q&A websites
@mastersthesis{msc_arthur,
title = {Studying Trends, Topics, and Duplicate Questions on Q&A Websites for Game Developers},
author = {Arthur V. Kamienski},
year = {2021},
date = {2021-09-29},
urldate = {2021-09-29},
school = {University of Alberta},
abstract = {The game development industry is growing and there is a high demand for develop-
ers 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 de-
velopment 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 Develop-
ment 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 web-
sites for game developers by automatically identifying duplicate questions. Duplicate
questions have a negative impact on Q&A websites by overloading them with ques-
tions 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 perfor-
mance 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.},
keywords = {Computer games, Q&A websites},
pubstate = {published},
tppubtype = {mastersthesis}
}
The game development industry is growing and there is a high demand for develop-
ers 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 de-
velopment 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 Develop-
ment 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 web-
sites for game developers by automatically identifying duplicate questions. Duplicate
questions have a negative impact on Q&A websites by overloading them with ques-
tions 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 perfor-
mance 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.
ers 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 de-
velopment 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 Develop-
ment 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 web-
sites for game developers by automatically identifying duplicate questions. Duplicate
questions have a negative impact on Q&A websites by overloading them with ques-
tions 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 perfor-
mance 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.