sate_ai 0.11.0

A fault injection framework for testing on-device AI models in Flutter. Simulate memory pressure, malformed inputs, and degradation to catch failures before deployment.

SATE AI

Fault Injection Framework for On-Device AI Models in Flutter

Overview

SATE AI is a fault injection framework for testing on-device AI models in Flutter and Dart. It simulates real-world failure scenarios — memory pressure, malformed inputs, quantization drift, thermal throttling, latency, model corruption, network failure, GPU memory pressure, and confidence degradation — so developers can validate model reliability before shipping to production.

Features

• 11 fault injectors covering memory, I/O, thermal, and network failure modes
• 8 model adapters: MockAdapter, OnnxAdapter, TFLiteAdapter, FllamaAdapter, MediaPipeAdapter, CoreMLAdapter, GoogleMLKitAdapter, and custom adapters
• CLI tool with subcommands for stress, benchmark, batch, schedule, serve, health-check, badge, and template output
• HTML report export with Chart.js charts
• Real-time SSE monitoring dashboard
• Golden baseline regression detection
• Stress test retries and flaky test detection
• SQLite storage for historical report tracking
• CI/CD status badges (SVG)
• Custom report templates (JSON/YAML)
• Retry and flaky test detection
• Stress scheduler (cron)
• VS Code extension

Installation

dependencies:
  sate_ai: ^0.11.0

flutter pub get

Quick Start

import 'package:sate_ai/sate_ai.dart';

Future<void> main() async {
  final model = MockAdapter(modelId: 'my-model');

  final report = await SateAI.stress(
    model: model,
    injectors: [
      MemoryPressureInjector(limitMb: 100),
      MalformedInputInjector(),
    ],
    retryCount: 3,
    flakyThreshold: 2,
  );

  if (report.passed) {
    print('Model passed all stress tests.');
  } else {
    print('Model failed: ${report.failureCount} failures.');
    print(report.toMarkdown());
  }
}

Fault Injectors

Injector	Fault Type
MemoryPressureInjector	memoryPressure
MalformedInputInjector	malformedInput
QuantizationDriftInjector	quantizationDrift
ThermalThrottleInjector	thermalThrottle
LatencyInjector	latency
ModelSwapInjector	modelSwap
NetworkLatencyDropInjector	networkFailure
ConfidenceThresholdInjector	confidenceValidation
GpuMemoryPressureInjector	gpuMemoryPressure
DataCorruptionInjector	dataCorruption
ModelVersionMismatchInjector	modelVersionMismatch

Model Adapters

Adapter	Runtime
MockAdapter	Pure Dart (testing)
OnnxAdapter	ONNX Runtime
TFLiteAdapter	TensorFlow Lite
FllamaAdapter	llama.cpp (Llama, Phi, Gemma)
MediaPipeAdapter	Google MediaPipe (vision)
CoreMLAdapter	Apple Core ML (iOS)
GoogleMLKitAdapter	Google ML Kit

CLI

# Basic stress test
sate_ai --model model.gguf --injectors memoryPressure,malformedInput

# With retry and flaky detection
sate_ai --model model.gguf --injectors memoryPressure --retry 3 --flaky-threshold 2

# Benchmark mode
sate_ai --model model.gguf --benchmark --benchmark-runs 20

# Batch mode with auto-detection
sate_ai --models model1.onnx,model2.tflite,model3.gguf --auto-detect

# Health check
sate_ai --model model.gguf --health-check

# HTML report
sate_ai --model model.gguf --injectors memoryPressure --html --output report.html

# Custom template
sate_ai --model model.gguf --injectors memoryPressure --template templates/slack.yaml

# Generate badges
sate_ai --model model.gguf --injectors memoryPressure --badge-output docs/sate_ai

# Save to SQLite
sate_ai --model model.gguf --injectors memoryPressure --db history.db

# List history
sate_ai --db history.db --db-history

# Golden baseline
sate_ai --model model.gguf --baseline
sate_ai --model model.gguf --compare --tolerance 5.0

# Real-time monitoring dashboard
sate_ai serve --port 8080

# Scheduling
sate_ai --model model.gguf --schedule "0 2 * * *"

# Compare two reports
sate_ai --compare-reports report1.json,report2.json --diff-html --diff-output diff.html

# Profiling
sate_ai --model model.gguf --profile --profile-runs 5

# Multi-language localized reports (en, es, fr, de, pt)
sate_ai --model model.gguf --markdown --language es --output report_es.md
sate_ai --model model.gguf --html --language fr --output report_fr.html

# Webhook notifications
sate_ai --model model.gguf --injectors memoryPressure \
  --webhook-url https://hooks.slack.com/services/XXX/YYY/ZZZ \
  --webhook-type slack

# Generate a custom injector
sate_ai create injector MyCustomInjector

Documentation

• Website
• API Reference
• Research Paper
• Contributing Guide

Contributing

Contributions are welcome. See CONTRIBUTING.md.

License

MIT — see LICENSE.