I Built a Fault Injection Framework for On-Device AI in Flutter - And You Can Help! 🧪
The Problem Nobody Talks About
On-device AI is exploding. Llama 3, Phi-3, Gemma — they're all running directly on phones and edge devices.
But here's the thing: nobody is testing what happens when these models fail.
When your AI model runs on a user's phone, it WILL fail:
- Memory pressure → OOM crashes
- Malformed input → Silent failures
- Quantization drift → Garbage outputs
- Thermal throttling → Slow inference
These issues only surface in production. Users experience crashes. Developers scramble to fix them. There's no tooling to test AI reliability before deployment.
Introducing SATE AI
So I built a framework to fix this.
SATE AI (Systematic AI Testing & Evaluation) is a fault injection framework for testing on-device AI models in Flutter applications.
What It Does
import 'package:sate_ai/sate_ai.dart';
final report = await SateAI.stress(
model: MockAdapter(), // Replace with your real model
injectors: [
MemoryPressureInjector(limitMb: 150),
MalformedInputInjector(),
],
);
if (report.passed) {
print('✅ Model passed stress test');
} else {
print('❌ Model failed: ${report.failures.length} failures');
print(report.toMarkdown());
}
Features
- ✅ Fault injection engine - Simulate memory pressure, malformed inputs, and more
- ✅ 59 unit tests - Full coverage of core modules
- ✅ MockAdapter - Test without real AI models
- ✅ StressReport - JSON + Markdown serialization
- ✅ Web Dashboard - Visualize test reports with charts
- ✅ Extensible - Add your own injectors and adapters
Watch It Catch a Failure
The dashboard shows exactly which faults caused the model to fail.
Why I Built This
I've been working with on-device AI and noticed a critical gap:
Every Flutter app tests its UI. Nobody tests its AI.
SATE AI is my attempt to make AI reliability testing as standard as UI testing.
The Tech Stack
- Flutter/Dart - 100% Dart, no production dependencies
- Mock Adapter - Pure Dart simulation for testing
- Chart.js - Web dashboard for report visualization
- GitHub Actions - CI/CD with 59 passing tests
Current Status
| Feature | Status |
|---|---|
| Core Framework | ✅ Complete |
| MemoryPressureInjector | ✅ Complete |
| MalformedInputInjector | ✅ Complete |
| Web Dashboard | ✅ Complete |
| ONNX Runtime Adapter | ⏳ In Progress |
| Quantization Drift Injector | ⏳ Planned |
| Thermal Throttle Injector | ⏳ Planned |
📦 Published on pub.dev
SATE AI is officially published!
dependencies:
sate_ai: ^0.1.0
🚀 How You Can Help
I'm building this solo, and I need YOUR help!
✅ Star the Repository
⭐ Star SATE AI on GitHub - It takes 2 seconds and helps others discover the project.
🔧 Pick a Good First Issue
We have 7 Good First Issues ready for contributors:
- Add ONNX Runtime Adapter - Medium
- Implement Quantization Drift Injector - Easy
- Add TensorFlow Lite Adapter - Medium
- Thermal Throttle Injector - Easy
- Add Confidence Score Validation - Easy
- Improve README Examples - Easy
- Create Web Dashboard - Completed! ✅
📝 Share Your Feedback
- Try SATE AI and open an issue
- Suggest new fault injectors
- Help improve documentation
- Share the project with others
💬 Join the Discussion
We have active Discussions where we talk about:
- Feature ideas
- Technical questions
- Show & Tell projects built with SATE AI
Why Contribute?
For Your Career
- Build your GitHub profile with real contributions
- Work with Flutter + AI (fast-growing field)
- Learn about fault injection and reliability testing
- Get your PR merged into a pub.dev package
For the Community
- Help make on-device AI more reliable
- Shape the direction of the project
- Be part of something from the ground up
Quick Start
# 1. Add the dependency
flutter pub add sate_ai
# 2. Write your first test
import 'package:sate_ai/sate_ai.dart';
final report = await SateAI.stress(
model: MockAdapter(),
injectors: [
MemoryPressureInjector(limitMb: 100),
MalformedInputInjector(),
],
);
print(report.passed ? '✅ Passed' : '❌ Failed');
📚 Resources
The Future of SATE AI
Here's what's coming:
| Feature | Planned | Status |
|---|---|---|
| ONNX Runtime Adapter | v0.2.0 | In Progress |
| Quantization Drift | v0.2.0 | Planned |
| Thermal Throttle | v0.2.0 | Planned |
| GitHub Action | v0.3.0 | Planned |
| Research Paper | 2026 | Planned |
| More Injectors | Ongoing | Planned |
💡 My Journey
I built this project in ONE DAY:
- 59 tests passing
- 2 working injectors
- Professional documentation
- Published to pub.dev
- First contributor already!
Open source is about community. I can't build this alone.
⭐ Star the repo. Share this post. Pick an issue.
Every contribution counts - from reporting bugs to writing code to sharing the project with others.
Built on research from SATE/AndroTest24. Let's make on-device AI reliable! 🚀
---
## 📋 Post Settings
| Setting | Value |
|---------|-------|
| **Title** | I Built a Fault Injection Framework for On-Device AI in Flutter - And You Can Help! |
| **Tags** | flutter, ai, testing, opensource |
| **Cover Image** | Upload a screenshot or GIF from the dashboard |
| **Series** | (Optional) "Building SATE AI" |
---
## 🎯 Publishing Checklist
- [ ] Copy the title
- [ ] Add tags: `flutter`, `ai`, `testing`, `opensource`
- [ ] Upload cover image
- [ ] Paste the entire body
- [ ] Click **"Publish"**
- [ ] Share the link on Twitter, LinkedIn, and Reddit
---
## 🔥 Bonus: Engagement Tactics
### After Publishing:
1. **Reply to every comment** within 24 hours
2. **Ping me on Twitter** - I'll retweet
3. **Share in Flutter Discord** - #showcase channel
4. **Post to r/FlutterDev** - Link to the DEV.to article
5. **Update your LinkedIn** - "I wrote about building SATE AI"
---
## 📊 Expected Impact
| Metric | Target | Timeline |
|--------|--------|----------|
| DEV.to Views | 1000+ | Week 1 |
| GitHub Stars | +20 | Week 1 |
| New Issues | +2 | Week 1 |
| Contributors | +1 | Week 2 |
---
## 🚀 Ready to Publish!
**Copy everything above and paste it into DEV.to!**
Let me know when you've published it and I'll help you with the next steps! 🎉
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