Software Instrumentation of Computer and Video Games

1
Software Instrumentationof Computer and Video
Games

• T. Bullen and M. KatchabawDepartment of Computer
  ScienceThe University of Western Ontario
• N. Dyer-WithefordFaculty of Information and
  Media StudiesThe University of Western Ontario

2
Introduction

• During the runtime of an application, it is often
  beneficial to have management capabilities
• For monitoring purposes
• For control purposes
• Having these capabilities enables us to sense the
  users experience, as well as tune application
  behaviour in order to improve this experience

3
Introduction

• 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

4
Introduction

• To demonstrate the usefulness of software
  instrumentation to computer and video games, we
  present a case study in automating content
  analyses of games
• 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

5
Approach to Instrumentation
6
Automating Content Analysis

• Content analyses of video games involve coding,
  enumerating, and statistically analyzing various
  elements and characteristics of games
• 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
• It can be an important tool to many people
  dealing with various aspects of games and the
  games industry

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Automating Content Analysis

• Problems arise, however, when one tries to apply
  traditional content analysis processes, for
  example from film or television, to games
• 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

8
Automating Content Analysis

• In the end, with the limited time and resources
  often available, it is exceedingly difficult to
  perform thorough content analyses on even
  areasonable portion of games
• To address these problems, this case study
  examines automating the process of content
  analysis for computer and video games
• Through automation, it is hoped that time and
  resources can be used more efficiently
  andeffectively to permit more thorough studies

9
Automating Content Analysis

• To automate content analysis, we take advantage
  of the fact that, unlike other forms of media,
  games are software executing onsome kind of
  computing device
• This can permit two forms of automation
• 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

10
Proof of Concept

• As a proof of concept, we have used our
  instrumentation framework to instrument Epics
  Unreal Engine to enable automated content
  analyses of Unreal-based games
• 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
• Source level access to the engine was not
  available

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Proof of Concept
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Proof of Concept

• Sensors have been developed to collect a wide
  variety of data useful for content analyses
• 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

13
Proof of Concept

• Actuators are currently under development for a
  variety of purposes
• They are primarily intended at this point at
  overcoming issues introduced by non-linearityand
  interaction in the game
• 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

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Proof of Concept
Screen shot of content analysis mutator
configuration screen
15
Experiences and Discussion

• To validate our proof of concept system, we
  conducted several content analysis experiments on
  Unreal Tournament 2004
• This series of games is one of the most
  populardriven by the Unreal Engine
• It is a fairly robust and flexible First Person
  Shooter that has numerous gameplay options
• Several different game types and rule sets
• Individual and team-based games
• Single player, multiplayer, and spectator modes

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Experiences and DiscussionDeathmatch 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 02645
• Game Finished 03029
• Gameplay Elapsed (Seconds) 240.88
• AI Dialog 28
• Human Dialog 27
• ----------------------------------
• ----------All Player Stats--------
• Total Deaths 47
• Total Suicides 1
• Total Kills 46

• 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
• ----------------------------------

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Experiences and DiscussionOnslaught 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 231643
• Game Finished 233203
• Gameplay Elapsed (Seconds) 964.18
• AI Dialog 216
• Human Dialog 31
• ----------------------------------
• ----------All Player Stats--------
• Total Deaths 142
• Total Suicides 5
• Total Kills 137

• 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-------------

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Experiences and Discussion

• Quality of data
• Data collected through automation matched manual
  results, and in some cases was better
• Quantity of data
• 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
• We found that results could be very different
• Which is ultimately better?

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Concluding Remarks

• 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
• Initial experimentation with a proof of concept
  system demonstrates its usefulness and shows
  great promise for the future

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Concluding Remarks

• Directions for future work include the following
• 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