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Typed Temporal Interaction Features for Simulation-Backed Forecasting of Open-Source Game Release Incidents

Shayma Alkobaisi, Anas Ali

arXiv:2609.31647v1cs.LGcs.AI

TL;DR

Game quality depends on interactions across code, assets, and workflows, while conventional defect predictors often omit those relations and release-outbreak labels are difficult to establish. GAMEQUALGRAPH-Pilot tests a typed temporal forecasting protocol in seeded simulations, where it does not significantly outperform its strongest local comparator.

  • Problem

    Release-level game-quality forecasting must account for interactions across code, assets, and workflows, but defensible outbreak labels are not readily available.

    Source: §I · p. 1

  • Method

    GAMEQUALGRAPH-Pilot summarizes typed temporal release evidence, estimates calibrated thirty-day incident risk, and ranks releases under a fixed testing budget.

    Source: §I · p. 1

  • Results

    0.520 AUPRC for GAMEQUALGRAPH-Pilot versus 0.522 for Static-Hetero-Reimpl; the -0.002 difference is not significant.

    Source: §I · p. 1

  • Takeaways & Limitations

    The controlled study supports a reproducible evaluation protocol, not an empirical claim of superiority or effectiveness on open-source games.

    Source: §I · p. 2, C. Heterogeneous and temporal graphs · p. 2

    The target is simulated rather than based on manually audited incident evidence, so real release reconstruction and stratified label audits remain required.

    Source: B. Validity · p. 6, C. Submission gate and next study · p. 7

Abstract

from arXiv · show

Open-source video-game quality depends on inter-actions among code, assets, configuration, tests, contributors, and issue workflows, yet conventional defect predictors usually flatten or omit these relations. We investigate release-level forecasting of a quality incident within thirty days using GAMEQUALGRAPH-Pilot, a typed temporal feature pipeline with calibrated risk estimates and effort-aware ranking. Because the accessible OS-SGameBench materials do not provide manually audited release dates and outbreak labels, the executed evaluation is explicitly simulation-backed rather than an empirical claim about real games. Five seeded worlds each contain 120 projects and 24 releases, with project-disjoint validation and future cross-project testing. The pilot obtains an AUPRC of 0.520, AUROC of 0.673, Brier score of 0.207, and 29.68% effort-aware recall at a twenty-percent testing budget. Its closest local comparator, Static-Hetero-Reimpl, reaches 0.522 AUPRC; the -0.002 difference is not statistically significant after Holm correction. Inference requires 0.023 milliseconds per release in the measured environment. Ablations and controlled missingness, drift, engine, project-size, alert-threshold, and attribution analyses expose where typed interactions help and where they fail. Results support the reproducibility of the proposed protocol, not deployment effectiveness. Real OSSGameBench release reconstruction, stratified label audits, and official graph-model comparisons remain mandatory before journal submission or operational use in practice. This boundary protects research integrity and supports credible evaluation.

I. INTRODUCTION

Game releases coordinate code, assets, configuration, tests, contributors, and issue workflows, but release-level quality evidence and defensible outbreak labels remain difficult to establish. GAMEQUALGRAPH-Pilot tests a typed temporal forecasting protocol in simulation, where it does not outperform its strongest local comparator.

I. INTRODUCTION

  • Release risk can arise from interactions among game artifacts that change-level predictors do not capture, while release tags, severity labels, and missing links complicate outbreak labeling.Conventional change-level signals such as churn, experience, and diffusion remain valuable, but games’ assets are not merely code documentation.Source: §I · p. 1
  • The study asks whether a leakage-resistant typed temporal protocol can be implemented and stress-tested before constructing the missing empirical target.GAMEQUALGRAPH-Pilot summarizes code, asset, testing, social, issue–PR, and lag relations; simulation tests pipeline behavior but cannot establish accuracy on open-source games.Source: §I · p. 1
  • 0.520 AUPRC for GAMEQUALGRAPH-Pilot versus 0.522 for Static-Hetero-Reimpl; the -0.002 difference is not significant.The evaluation uses five seeded worlds with project-disjoint validation and a future cross-project holdout.Source: §I · p. 1
  • The introduction contributes a formal release-level risk formulation, an executable simulation benchmark with project-and-time separation, and a falsification of automatic superiority in the controlled setting.The benchmark includes five seeds, ablations, controlled shifts, synchronized exports, and claim-level provenance; the result defines requirements for a real-data study.Source: §I · pp. 1–2

II. RELATED WORK

Prior game-quality resources cover distinct evidence types, while release-level forecasting still requires defensible cutoffs, audited outcomes, typed prerelease evidence, and effort-aware evaluation. The paper formalizes a cutoff-based target, but its incident labels are generated in simulation rather than inferred from OSSGameBench.

A. Game-development evidence

  • Game-development datasets cover postmortems, executable testing, visible glitches, and repository activity, but OSSGameBench does not designate player-facing releases or high-impact incident outbreaks.The cited dataset descriptions report repository activity and traceability, not the target labels required for this forecasting task.Source: A. Game-development evidence · p. 2

B. Defect prediction and representation learning

  • JIT predictors, learned code representations, and tree ensembles address change-level prediction, but their units and evidence do not by themselves solve release-label forecasting.Cross-project prediction is sensitive to distribution differences and data quality.Source: B. Defect prediction and… · p. 2

C. Heterogeneous and temporal graphs

  • The unresolved graph-learning gap is a defensible release-time cutoff, audited incident outcome, prerelease typed evidence, and testing-effort-aware evaluation—not simply applying HGT to games.The pilot validates the protocol and software path while leaving empirical effectiveness unanswered.Source: C. Heterogeneous and temporal graphs · p. 2

A. Entities, cutoff, and outcome

  • The formulation restricts graph evidence to events timestamped by the candidate-release cutoff and defines typed nodes and timestamped relations.Its binary target counts validated high-impact incidents in the following 30 days against a preregistered project-aware outbreak threshold; in this study, that target is generated synthetically.Source: A. Entities, cutoff, and outcome · p. 2

B. Typed temporal representation

The representation section describes an ideal heterogeneous encoder that transforms modalities, ages events by relation, summarizes messages, and fuses them into a release state. The executed pilot instead uses fixed typed summaries and interaction terms.

B. Typed temporal representation

  • The ideal representation transforms raw modalities, applies relation-specific event-age decay, summarizes relation messages, and fuses them into an intended learned release state.The pilot replaces these equations with fixed typed summaries and interaction terms, so the tree ensemble is not described as a graph neural network.Source: B. Typed temporal representation · p. 2

C. Risk, calibration, and constrained utility

The section defines leakage-resistant forecasting for future releases of unseen projects, then describes calibrated risk estimates and effort-aware testing under a fixed budget.

A. Architecture

GAMEQUALGRAPH-Pilot converts multimodal repository evidence into timestamp-valid release graphs, calibrated forecasts, and effort-aware testing decisions.

  • The executed pipeline feeds 20 base release features and ten typed interaction summaries to histogram gradient boosting, not an HGT or temporal GNN.The design is compatible with a future HGT or temporal GNN, but the executed model is deliberately lighter.Source: A. Architecture · p. 3
  • Ten summaries expose code–asset coupling, asset–test mismatch, review, contributor dispersion, issue–PR traceability, testing gaps, temporal pressure, bursts, graph density, and relation entropy.Source: A. Architecture · p. 3
  • Streaming graph construction costs O(|V|+|E|) time and space per retained snapshot; duplicate repositories, forks, aliases, and late-linked fixes remain untested.Source: A. Architecture · p. 3

B. Training and operational inference

Training and evaluation separate projects and future releases; operational inference uses histogram boosting and effort-based ranking, with several stated failure modes.

B. Training and operational inference

  • Projects 0–79 train, 80–99 provide calibration and configuration evidence, and 100–119 are tested only at releases 16–23.No event or label from a test project enters fitting.Source: B. Training and operational inference · p. 4
  • Inference costs O(TD) per release and ranking adds O(n log n); failure modes include missing links, unobserved releases, label heterogeneity, sparse projects, strategic triage, and unrepresented abrupt changes.Source: B. Training and operational inference · p. 4

A. Evidence and simulation

The evaluation uses synthetic temporal worlds with project-disjoint and future-project testing, local comparators, and robustness analyses across missingness, drift, and strata.

A. Evidence and simulation

  • Each seed generates 120 projects with 24 releases; dependence, post-release-18 drift, and incident mechanisms are simulated, with project-disjoint validation and future-project testing.Source: A. Evidence and simulation · p. 4

B. Comparators and configurations

C. Metrics and statistics

  • AUPRC is the primary metric; five-seed paired differences use Wilcoxon tests and Holm adjustment, but p-values are descriptive because power is limited.Source: C. Metrics and statistics · p. 5
  • Robustness analyses vary relation missingness, thresholds and budgets, engine and project size, post-drift releases, modality groups, and permutation attribution.Player reviews, language embeddings, audio pixels, screenshots, store mappings, and real human judgments are not included.Source: C. Metrics and statistics · p. 5

VI. RESULTS

On simulated future cross-project holdouts, GAMEQUALGRAPH-Pilot did not improve main discrimination over its strongest local comparator. The results also report effort-budget behavior, robustness analyses, and measured inference cost.

A. Predictive and calibration performance

  • 0.520 AUPRC and 0.673 AUROC for GAMEQUALGRAPH-Pilot; Static-Hetero-Reimpl reached 0.522 AUPRC, while the pilot's Brier score was 0.207 and ECE was 0.071.Table V summarizes the synchronized aggregates across five seeded simulations.Source: A. Predictive and calibration… · p. 5, Table V · p. 6

B. Budget behavior and statistical comparison

  • The pilot captured 29.68% of incidents at a 20% effort budget; no comparison met the 0.05 threshold after Holm correction.Fig. 3 separates alert-threshold precision–recall trade-offs from incident recall across effort budgets; this simulated utility does not establish maintainer effort savings.Source: B. Budget behavior and statistical… · p. 5, Figure 3 · p. 6

C. Generalization, ablation, and attribution

  • The pilot's small ablation change and distributed permutation importance do not establish that typed relations improve discrimination or explain real defects.Engine labels are simulated categories, so those comparisons test code paths rather than populations.Source: C. Generalization, ablation, and… · p. 5

D. Missingness, drift, and efficiency

  • AUPRC declines as typed evidence is masked, and mean measured inference is 0.023 ms per release after feature construction.The per-release curve around the programmed release-18 shift is noisy and does not show real-world drift adaptation; measured latency excludes repository cloning, parsing, embedding, and full graph construction.Source: D. Missingness, drift, and efficiency · p. 5, Figure 2 · p. 6

VII. DISCUSSION AND THREATS TO VALIDITY

The simulation supports the end-to-end protocol more strongly than a claim about the model's effectiveness. The authors identify construct, internal, external, and statistical validity limits, alongside deployment ethics risks.

A. Interpretation

  • The full feature set did not beat the static heterogeneous comparator; redundancy and the 160-release test folds are possible explanations, not proven causes.The proposed next model would use event-level nodes and relation-specific time encodings, with official heterogeneous and temporal implementations as matched-budget comparators.Source: A. Interpretation · p. 6

B. Validity

  • The target comes from a generated equation rather than manually audited release incidents, so the pilot cannot establish real-game incident prediction effectiveness.The proposed real study requires a predefined outbreak rubric, stratified double-coding, agreement reporting, and measurement of missing links.Source: B. Validity · p. 6
  • Simulation may favor the proposed summaries because the generator shares their concepts, although all methods receive appropriate feature subsets.The source exposes coefficients and records zero real labeled releases.Source: B. Validity · p. 6
  • The results do not generalize to OSSGameBench projects, proprietary studios, stores, game engines, or player populations; engine and size strata are simulated.Review aggregates and GH Archive histories are absent from the executed study.Source: B. Validity · p. 6
  • Five worlds limit statistical-conclusion validity; paired tests have coarse attainable p-values, and runtime excludes data acquisition and content encoding.Multiple comparisons are Holm-adjusted, and nonsignificant differences are not described as trends.Source: B. Validity · p. 7
  • Deployment could stigmatize contributors or encourage incident concealment, so future systems should separate testing allocation from personnel evaluation.The authors also recommend artifact-level explanations, contestable predictions, and avoiding protected or identity-proxy features.Source: B. Validity · p. 7

C. Submission gate and next study

The package is not ready for empirical submission or deployment: real release-outbreak labels and official comparisons are still needed. The conclusion supports reproducibility of the protocol, not predictive superiority or operational effectiveness.

C. Submission gate and next study

  • Four prerequisites are complete authorized benchmark payload, stratified audits of repository-to-release mappings and 30-day labels, two official baselines, and event-level temporal heterogeneous learning with cross-project and temporal evaluation.Source: C. Submission gate and next study · p. 7
  • Only after these steps can effect estimates, calibration, explanations, and operational savings be interpreted for open-source games.Source: C. Submission gate and next study · p. 7
  • Empirical Software Engineering is identified as the closest eventual venue, but venue quartile status and policies require rechecking at submission.Source: C. Submission gate and next study · p. 7

VIII. CONCLUSION

  • Without manually audited real release-outbreak labels, the simulation-backed package does not establish empirical forecasting effectiveness and is not ready for submission or deployment.Source: §VIII · p. 8
  • The implemented estimator uses fixed relation summaries rather than an event-level heterogeneous graph neural network, constraining the current conclusion to a reproducible scaffold.Source: §VIII · p. 8
  • Future work should reconstruct and double-code OSSGameBench releases, quantify incomplete links, safely incorporate asset content, and evaluate calibration and testing utility across unseen projects.Source: §VIII · p. 8
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