Download Software Instrumentation of Computer and Video Games

Software Instrumentation
of Computer and Video Games
T. Bullen and M. Katchabaw
Department of Computer Science
The University of Western Ontario
N. Dyer-Witheford
Faculty of Information and
Media Studies
The University of Western Ontario
Introduction
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During the runtime of an application, it is often
beneficial to have management capabilities
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For monitoring purposes
For control purposes
Having these capabilities enables us to sense
the user’s experience, as well as tune application
behaviour in order to improve this experience
Introduction
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Computer and video game software is no
different from traditional applications with respect
to the need for manageability
Except, of course, that very few have carried out
research and development in this area to date
This is unfortunate considering both the
interesting problems that can be addressed
using management techniques and the need for
solutions to these unique problems
Introduction
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To demonstrate the usefulness of software
instrumentation to computer and video games,
we present a case study in automating content
analyses of games
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This includes a proof of concept system we developed
using the Unreal Engine, and a discussion of our
experiences in using this system to date
First, however, we will examine our approach to
software instrumentation …
Approach to Instrumentation
Coordinator
Actuator 1
Sensor 1
Game
Object 1
Sensor 2
Game Application
Code
Game
Object 3
Sensor j
Game
Object 2
Actuator 2
Game
Object i
Actuator k
Automating Content Analysis
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Content analyses of video games involve coding,
enumerating, and statistically analyzing various
elements and characteristics of games
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This includes violence, offensive language, sexual
content, gender and racial inclusiveness, and so on
While content analysis has its limitations, it is
invaluable in providing a quantitative assessment
of games to go with more qualitative analyses
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It can be an important tool to many people dealing
with various aspects of games and the games industry
Automating Content Analysis
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Problems arise, however, when one tries to apply
traditional content analysis processes, for
example from film or television, to games
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Processes are manual and are consequently time
consuming and labour-intensive
This tends to result in significantly reduced play times
or limiting analyses to only a very few games
Traditional analyses also tend not to consider
interactivity and non-linearity that occurs in games
The rapid rate at which games are released and the
industry evolves makes keeping up difficult
Automating Content Analysis
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In the end, with the limited time and resources
often available, it is exceedingly difficult to
perform thorough content analyses on even a
reasonable portion of games
To address these problems, this case study
examines automating the process of content
analysis for computer and video games
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Through automation, it is hoped that time and
resources can be used more efficiently and
effectively to permit more thorough studies
Automating Content Analysis
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To automate content analysis, we take
advantage of the fact that, unlike other forms
of media, games are software executing on
some kind of computing device
This can permit two forms of automation:
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Partial automation: software executing along side the
game monitors game execution and collects and
reports the data normally collected manually
Full automation: further software elements take the
role of the player and generate gameplay experiences
without the need for a human player
Proof of Concept
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As a proof of concept, we have used our
instrumentation framework to instrument
Epic’s Unreal Engine to enable automated
content analyses of Unreal-based games
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Unreal is a popular engine amongst professional and
amateur developers, providing numerous possible
games for content analysis experiments
Instrumentation was implemented using the
UnrealScript language
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Source level access to the engine was not available
Proof of Concept
Game Info
Game Rules
Sensor
Mutators
Coordinator
Game
Object
Proof of Concept
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Sensors have been developed to collect a wide
variety of data useful for content analyses:
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Death of characters, weapon use by characters,
vehicular offences, use of offensive language, gender
and racial diversity in characters, and a variety of
other game statistics
Data can be reported throughout a game or only as
summaries at the end of games
Sensors can be configured at run-time to tailor
the data collected to the needs of the content
analyses being conducted
Proof of Concept
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Actuators are currently under development for
a variety of purposes
They are primarily intended at this point at
overcoming issues introduced by non-linearity
and interaction in the game
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For example, this can force the completion of
gameplay elements and objectives to control the flow
of action in the game
This can enable more thorough and efficient content
analyses than what was previously possible
Proof of Concept
Screen shot of content analysis mutator configuration screen
Experiences and Discussion
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To validate our proof of concept system, we
conducted several content analysis experiments
on Unreal Tournament 2004
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This series of games is one of the most popular
driven by the Unreal Engine
It is a fairly robust and flexible First Person
Shooter that has numerous gameplay options
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Several different game types and rule sets
Individual and team-based games
Single player, multiplayer, and spectator modes
Experiences and Discussion:
Deathmatch Game
------------Level Info-----------Level Name: Rrajigar
Game Type: DeathMatch
Total Players: 14
AI Players: 13
Human Players: 1
Spectators: 0
Male Players: 13
Female Players: 1
Level Loaded: 0:26:45
Game Finished: 0:30:29
Gameplay Elapsed (Seconds): 240.88
AI Dialog: 28
Human Dialog: 27
-------------------------------------------All Player Stats-------Total Deaths: 47
Total Suicides: 1
Total Kills: 46
Total AI Deaths: 46
Total Human Deaths: 1
Total Deaths Caused By AIs: 22
Total Deaths Caused By Humans: 25
Total Female Deaths: 12
Total Male Deaths: 35
Total Deaths Caused By Females: 6
Total Deaths Caused By Males: 41
-----------------------------------------Local Player Stats-------Player Deaths: 1
Player Suicides: 0
Player Killed: 1
Deaths Caused By Player: 25
Player Killed By AI: 1
Player Killed By Human: 0
Player Killed By Male: 1
Player Killed By Female: 0
AI Deaths Caused By Player: 25
Human Deaths Caused By Player: 0
Female Deaths Caused By Player: 7
Male Deaths Caused By Player: 18
Deaths Witnessed By Player: 29
----------------------------------------------Expletives----------ass: 2
----------------------------------
Experiences and Discussion:
Onslaught Game
------------Level Info-----------Level Name: Arctic Stronghold
Game Type: Onslaught
Total Players: 12
AI Players: 11
Human Players: 1
Spectators: 0
Male Players: 9
Female Players: 3
Level Loaded: 23:16:43
Game Finished: 23:32:03
Gameplay Elapsed (Seconds): 964.18
AI Dialog: 216
Human Dialog: 31
-------------------------------------------All Player Stats-------Total Deaths: 142
Total Suicides: 5
Total Kills: 137
Total AI Deaths: 138
Total Human Deaths: 4
Total Deaths Caused By AIs: 119
Total Deaths Caused By Humans: 23
Total Female Deaths: 26
Total Male Deaths: 116
Total Deaths Caused By Females: 8
Total Deaths Caused By Males: 134
-----------------------------------------Local Player Stats-------Player Deaths: 4
Player Killed: 4
Deaths Caused By Player: 23
Player Killed By AI: 4
Player Killed By Human: 0
Player Killed By Male: 4
Player Killed By Female: 0
AI Deaths Caused By Player: 23
Human Deaths Caused By Player: 0
Female Deaths Caused By Player: 9
Male Deaths Caused By Player: 14
Deaths Witnessed By Player: 43
---------------------------------------------Team Info------------Female Allies: 1
Male Allies: 4
Friendly Fire Deaths: 5
Allies Killed By Player: 0
Player Killed By Ally: 0
Experiences and Discussion
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Quality of data
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Quantity of data
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Data collected through automation matched manual
results, and in some cases was better
We found that we could collect massive amounts of
data with no visible impact on gameplay, even when
data was reported in real-time throughout a game
Partial versus fully automated analyses
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We found that results could be very different
Which is ultimately better?
Concluding Remarks
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Management of computer and video games is a
potentially rich, yet unexploited area for research
and development
Our own work in automating content analyses
through software instrumentation represents an
interesting step in this direction
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Initial experimentation with a proof of concept system
demonstrates its usefulness and shows great promise
for the future
Concluding Remarks
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Directions for future work include the following:
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Continuing research, development, and
experimentation in automating content analysis of
game software
Exploring how this software instrumentation can be
adapted and extended for use in other computer and
video games for other purposes
Investigating how other tools and techniques of
application management can be made use of in
computer and video game software