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Record W2536554064 · doi:10.1109/mere.2008.5

Requirements in Conflict: Player vs. Designer vs. Cheater

2008· article· en· W2536554064 on OpenAlexaff
David Callele, Eric Neufeld, Kevin A. Schneider

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Malware Detection Techniques
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsNegotiationComputer scienceRequirements engineeringStakeholderSecurity engineeringRequirements analysisProcess (computing)Domain (mathematical analysis)Context (archaeology)Requirements elicitationComputer securitySecurity serviceSoftware security assuranceInformation securityPublic relations

Abstract

fetched live from OpenAlex

There are significant interactions between video game stakeholder emotional requirements and security requirements. Counter-intuitively, some traditional security requirements are not necessarily met by the game implementation some forms of security breaches are condoned by the stakeholders (if not actually demanded by them) and the requirements engineering process must support these contradictions. We present an overview of security requirements for video games and show how stakeholder diversity introduces significant complexities to the requirements negotiation process. Our analysis of certain security threats, and their emotional motivations, shows that these motivations form an important element of the emotional requirements and that significant context is necessary for properly capturing the emotional requirements related to security. Finally, we show how emotional requirements can be used to guide security goal development for this domain and propose the use of in-game justice systems to allow players to address security violations in realtime.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.800
Threshold uncertainty score0.479

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.051
GPT teacher head0.285
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2008
Admission routes1
Has abstractyes

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