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Usage.md

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Usage Guide

Command Structure

sysmobench --task <task> --method <method> --model <model> --metric <metric> [options]

Required Parameters

  • --task - System: spin, mutex, rwmutex, etcd, redisraft, curp, dqueue, locksvc, raftkvs
  • --method - Generation method: direct_call, agent_based, trace_based
  • --model - Model name (configured in config/models.yaml)

Common Parameters

  • --metric - Evaluation metric
  • --spec-file <path> - Use existing TLA+ spec (skip generation)
  • --config-file <path> - Use existing TLC config (skip generation)

Batch Evaluation

  • --tasks - Multiple tasks (space-separated)
  • --metrics - Multiple metrics
  • --output <dir> - Output directory (default: results/)

List Options

sysmobench --list-tasks
sysmobench --list-methods
sysmobench --list-models
sysmobench --list-metrics

Metrics

SysMoBench evaluates models across four dimensions:

1. Syntax Correctness

Metric Description Parameters
compilation_check Full-model compilation with SANY None
action_decomposition Per-action validation with recovery None

2. Runtime Correctness

Metric Description Parameters
runtime_check Model checking without invariants --tlc-timeout <seconds>
coverage Action coverage via TLC statistics --tlc-timeout <seconds>
runtime_coverage Simulation-based coverage --tlc-timeout <seconds>

3. Conformance to Implementation

Metric Description Applies To Parameters
trace_validation Trace generation and validation spin, mutex, rwmutex, etcd, redisraft, curp --with-exist-traces <N>
--with-exist-specTrace
--create-mapping
pgo_trace_validation Trace validation for PGo systems dqueue, locksvc, raftkvs --with-exist-traces <N>

4. Invariant Correctness

Metric Description Parameters
invariant_verification Verify system-specific safety and liveness properties --tlc-timeout <seconds>

Composite

Metric Description Parameters
composite Sequential evaluation across all dimensions None

Model Configuration

File Location

config/models.yaml

Configuration Format

models:
  <model_name>:
    provider: "openai" | "anthropic" | "genai" | "deepseek" | "yunwu"
    model_name: "<actual-model-name>"
    api_key_env: "<ENV_VAR_NAME>"
    temperature: <float>
    max_tokens: <int>
    timeout: <int>        # seconds, optional
    top_p: <float>        # optional
    url: "<endpoint>"     # optional, for custom endpoints

Examples

models:
  # Anthropic Claude
  claude:
    provider: "anthropic"
    model_name: "claude-sonnet-4-20250514"
    api_key_env: "ANTHROPIC_API_KEY"
    temperature: 0.1
    max_tokens: 64000
    top_p: 0.9

Usage

  1. Add model configuration to config/models.yaml
  2. Set environment variable: 
    export YOUR_API_KEY="key"
  3. Use model name in command: 
    sysmobench --task spin --method direct_call --model custom --metric compilation_check

Output Structure

Results in output/<metric>/<task>/<method>_<model>/:

output/coverage/spin/direct_call_gemini/
├── generated_spec.tla
├── generated_config.cfg
├── evaluation_results.json
└── tlc_output.log

Task Configuration

Task configs in tla_eval/tasks/<task>/task.yaml:

name: "spin"
description: "Asterinas OS spinlock"
system_type: "concurrent"
language: "rust"

repository:
  url: "https://github.com/asterinas/asterinas.git"
  branch: "main"

source_files:
  - path: "ostd/src/sync/spin.rs"

default_source_file: "ostd/src/sync/spin.rs"
specModule: "spin"
traces_folder: "data/sys_traces/spin"
...