SnapBug
SnapBug is the world’s first autonomous game quality assurance and self-healing defect triage platform built specifically for game developers and studio QA teams. Game production is notoriously difficult because games are complex, non-deterministic simulations where high-velocity physics and state machines collide unpredictably. Traditional testing relies on manual playtesters, draining up to forty percent of engineering budgets while leaving developers chasing vague player reports like the game breaking in Sector Four. SnapBug eliminates this bottleneck by automating the entire lifecycle of discovering, diagnosing, and repairing gameplay defects. At its core, SnapBug’s Native Windows Execution Worker spawns, monitors, and asserts real compiled Windows executables locally or inside an isolated Windows Sandbox. It tracks genuine Win32 standard I/O, exit codes, RAM, CPU load, and active window handles without synthetic mocks. SnapBug provides four execution profiles: Profile A monitors process stability; Profile B drives Win32 window automation and synthetic inputs; Profile C streams live physics vectors via our lightweight Godot and Unity SnapBugAdapter SDK; and Profile D introduces our autonomous Chaos Fuzzing Engine, which spams stochastic inputs to uncover hidden collision boundary edge cases. When an anomaly occurs, our Scene Attribution Engine correlates runtime logs to pinpoint the exact game scene file. Simultaneously, our Computer Vision Defect Grounding Engine scans gameplay framebuffers, automatically drawing pixel-accurate bounding boxes around wall tunneling, ghost hitboxes, and HUD overflows. Groq AI models diagnose root causes in seconds, delivering rank-ordered hypotheses. Finally, our Self-Healing Auto-PR Engine synthesizes production-ready unified code diffs in GDScript or C sharp, allowing developers to review colorized diffs and click a single Apply Patch button to resolve the defect instantly.
Preliminary score
- Value for the user15.5 / 25
A specific problem and a clear outcome from solving it.
- Prototype and use of AI11.0 / 30
A working core scenario, and what AI actually contributes to the result.
- Quality testing3.5 / 20
Tests, examples of failures, and comparison with the current approach.
- Feasibility3.5 / 15
Data requirements, running costs and a clear next step.
- Originality6.0 / 10
A meaningful difference from common solutions.
What the judges wrote
- Claude Opus 5.5Preliminary37/100
- Strengths
- Your target user, game studio QA teams chasing vague player bug reports, is real and specific, and the pain of manual playtesting is well articulated. Your pipeline concept of running real compiled executables, fuzzing inputs, grounding defects visually, diagnosing with an LLM, and proposing a GDScript/C# patch hangs together as an end-to-end idea. Tying runtime anomalies back to a specific scene file is a thoughtful step beyond a generic chatbot.
- To improve
- Your submission describes a very large system but gives no evidence that any part of it runs. Show one concrete scenario end to end, for example a Godot build with a known wall-tunneling bug, the detected frame with its bounding box, the diagnosis, and the generated diff. Replace '30 of 30 tests passed' with what those tests were, at least one case where detection or the auto-patch failed, and how long the same bug takes a human QA tester to find today. Fill in data, running costs (LLM calls and sandbox time per run), a setup guide, and a realistic next step such as a pilot with one indie studio.
- GPT-6 AstraPreliminary42/100
- Strengths
- Your four execution profiles target a clear game-studio workflow, progressing from process monitoring to telemetry-driven testing and input fuzzing. Your proposed combination of scene attribution, engine telemetry, and reviewable code patches gives the project a useful game-specific focus beyond generic AI bug summarization.
- To improve
- Your written materials need a reproducible end-to-end example connecting a failing game run, captured evidence, an AI diagnosis, a patch, and a verified rerun. For your reported 30 tests, show concrete inputs, assertions, results, and failures discovered; the pass count alone does not establish coverage or defect-detection accuracy. Compare detection and triage time against manual QA, including false positives and incorrect patches. Clarify your runtime model stack, required telemetry and source access, per-run costs, and a realistic next validation milestone.
The team
Fərid HəşimliLead
developer
Hüseyn Əhmədzadə
developer