autonomy-self-evolving-testing-loop

---
name: autonomy-self-evolving-testing-loop
description: Self-evolving simulation-based testing loop for autonomous cyber-physical systems. Continuous scenario generation, execution, and telemetry analysis.
version: 1.0.0
author: Research Synthesis
license: MIT
metadata:
  hermes:
    tags: [autonomous systems testing, self-evolving simulation, cyber-physical systems, continuous testing, scenario generation]
    source_paper: "AutonomyLens: A Self-Evolving Simulation-Based Testing Loop for Autonomous Systems (arXiv:2604.11672)"
    citations: 0
    category: software engineering
---

# 自主系统自进化仿真测试循环 (Autonomy Self-Evolving Testing Loop)

## 概述
AutonomyLens是一个自进化的基于仿真的测试循环框架，用于自主网络物理系统(如无人机)的验证。将场景设计、仿真执行和遥测分析整合为统一的自进化循环。

## 核心创新

### 1. 自进化测试循环
```python
class AutonomyLens:
    def __init__(self, simulator, coverage_analyzer):
        self.sim = simulator
        self.coverage = coverage_analyzer
        self.scenario_db = ScenarioDatabase()
        
    def testing_loop(self, iterations=100):
        for i in range(iterations):
            # 1. 场景生成/选择
            scenario = self.generate_or_select_scenario()
            
            # 2. 仿真执行
            telemetry = self.sim.execute(scenario)
            
            # 3. 分析
            coverage = self.coverage.analyze(telemetry)
            failures = self.detect_failures(telemetry)
            
            # 4. 进化
            if coverage < threshold or failures:
                self.evolve_scenarios(scenario, failures)
            
            # 5. 更新知识库
            self.scenario_db.update(scenario, coverage, failures)
    
    def evolve_scenarios(self, base_scenario, failure_points):
        # 基于失败的场景进化
        mutations = [
            self.add_environment_complexity(base_scenario),
            self.stress_failure_conditions(base_scenario, failure_points),
            self.combine_scenarios(base_scenario)
        ]
        return mutations
```

### 2. 覆盖引导的场景生成
- **结构覆盖**: 代码路径覆盖
- **语义覆盖**: 场景类型多样性
- **故障覆盖**: 边界条件探索

### 3. 遥测分析
- **异常检测**: 识别异常行为
- **根因分析**: 定位失败原因
- **回归检测**: 跟踪性能退化

## 应用场景
- **自动驾驶**: 虚拟测试里程积累
- **无人机系统**: 飞行安全验证
- **工业机器人**: 协作安全测试

## 测试循环架构
```
┌─────────────────────────────────────┐
│     Scenario Generation/Selection   │
└──────────────┬──────────────────────┘
               ▼
┌─────────────────────────────────────┐
│        Simulation Execution         │
│    ┌──────────┐    ┌──────────┐    │
│    │  Physics │    │  Agent   │    │
│    │  Engine  │    │  System  │    │
│    └──────────┘    └──────────┘    │
└──────────────┬──────────────────────┘
               ▼
┌─────────────────────────────────────┐
│       Telemetry Analysis            │
│  ┌──────────┐    ┌──────────┐      │
│  │ Coverage │    │ Failure  │      │
│  │ Analysis │    │ Detection│      │
│  └──────────┘    └──────────┘      │
└──────────────┬──────────────────────┘
               ▼
┌─────────────────────────────────────┐
│     Scenario Evolution              │
│  (反馈到场景生成)                    │
└─────────────────────────────────────┘
```

## 激活关键词
- 自主系统测试
- 自进化仿真
- 场景进化
- autonomy testing loop
- self-evolving simulation

## 参考文献
- Agrawal, A., Garapati, J., & Zhang, B. (2026). AutonomyLens: A Self-Evolving Simulation-Based Testing Loop for Autonomous Systems. arXiv:2604.11672.