data/processed/RQ1/GameBuilder/reference_trajectory.yaml

# Complete Reference Trajectory Configuration - GameBuilder Project
#
# Version: Ideal trajectory based on code design (applicable to all models)
# Principle: Only includes steps explicitly required by code design, not based on test statistics
#
# Code Design Analysis:
# 1. Three Agents execute sequentially:
#    - Senior Software Engineer: Generate game code
#    - Software Quality Control Engineer: Review code (has validate_python_code tool)
#    - Chief Software Quality Control Engineer: Final evaluation (has validate_python_code tool)
#
# 2. Tool usage analysis:
#    - validate_python_code tool is specifically configured for QA and Chief QA
#    - tasks.yaml requires "check for errors" and "syntax errors"
#    - Code validation is core QA responsibility, tool calls are necessary
#
# 3. Execution flow:
#    - Process.sequential: Sequential execution
#    - Each Agent executes corresponding Task, generates results through LLM

# Project name
project_name: "GameBuilder"

# Trajectory extraction type configuration
# As per user requirements, focus on: SPAN, Chain, AGENT, LLM, Tool
# Not concerned with: Task Created, Crew Created and other non-core content
extract_types:
  - "SPAN" # Level 1: Top-level execution span
  - "Chain" # Level 2: Crew execution chain
  - "AGENT" # Level 3: Agent execution
  - "LLM" # Level 4: LLM calls
  - "Tool" # Level 4: Tool calls

# ============================================================================
# Reference Trajectory (Ground Truth) - Based on code design
# ============================================================================
#
# Graphical structure (ideal execution path):
#
# [SPAN] GameBuilder_generation
# └─ [Chain] Crew***.kickoff
#    ├─ [AGENT] Senior Software Engineer  ← Task 1: code_task
#    │  └─ [LLM] *  ← Generate game code
#    │
#    ├─ [AGENT] Software Quality Control Engineer  ← Task 2: review_task
#    │  ├─ [LLM] *  ← Review code, decide to validate
#    │  ├─ [Tool] Python Code Validator  ← Validate code syntax
#    │  └─ [LLM] *  ← Process validation results and output
#    │
#    └─ [AGENT] Chief Software Quality Control Engineer  ← Task 3: evaluate_task
#       ├─ [LLM] *  ← Final evaluation, decide to validate
#       ├─ [Tool] Python Code Validator  ← Confirm code is runnable
#       └─ [LLM] *  ← Confirm completion and output
#
# Design rationale:
# - crew.py: Defines sequential execution flow of 3 Agents and 3 Tasks
# - tasks.yaml: All Tasks' expected_output is "full python code, only the python code"
# - tasks.yaml: review_task requires "check for errors" and "syntax errors"
# - main.py line 525: SPAN name is "GameBuilder_generation"
# - agents.yaml: QA specifically responsible for "checking code for errors"
# - crew.py: Both QA and Chief QA are configured with validate_python_code tool
#
# Ideal trajectory explanation:
# - Senior Engineer: Generate code (1 LLM call)
# - QA Engineer: Review code -> Validation tool -> Output (2 LLM + 1 tool)
# - Chief QA: Evaluate code -> Validation tool -> Confirm (2 LLM + 1 tool)
# - This trajectory represents ideal, no-retry, no-redundancy execution path
#
# Note:
# - "LLM: *" represents any LLM model (wildcard match)
# - Agent names must exactly match names defined in agents.yaml
# - Actual execution may include more LLM calls (thinking, retries, etc.), which is normal
# ============================================================================

reference_trajectory:
  # ===== Level 1: SPAN =====
  - "SPAN: GameBuilder_generation"

  # ===== Level 2: Chain (wildcard handling for UUID) =====
  - "Chain: Crew***.kickoff"

  # ===== Task 1: code_task =====
  # Senior Software Engineer generates game code
  - "Agent: Senior Software Engineer"
  - "LLM: *" # Any model

  # ===== Task 2: review_task =====
  # Software Quality Control Engineer reviews code
  - "Agent: Software Quality Control Engineer"
  - "LLM: *"
  - "Tool: Python Code Validator"
  - "LLM: *"

  # ===== Task 3: evaluate_task =====
  # Chief Software Quality Control Engineer final evaluation
  - "Agent: Chief Software Quality Control Engineer"
  - "LLM: *"
  - "Tool: Python Code Validator"
  - "LLM: *"

# Target tools list (for single-tool use metric)
# Only includes actual tool calls, used to detect tool usage
target_tools:
  - "Tool: Python Code Validator"

# Models to evaluate
models:
  - "GPT-5"
  - "GPT-4o-mini"
  - "DeepSeek-V3-1"
  - "DeepSeek-R1"
  - "Gemini-2.5-flash"
  - "Gemini-2.5-flash-nothinking"
  - "Qwen3-235b"
# ============================================================================
# Usage Instructions
# ============================================================================
#
# 1. Trajectory hierarchy:
#    - Level 1: SPAN (GameBuilder_generation)
#    - Level 2: Chain (Crew***.kickoff, wildcard handles UUID)
#    - Level 3: AGENT (specific Agent names, must match exactly)
#    - Level 4: LLM (wildcard matches any model)
#
# 2. Wildcard handling:
#    - Crew_<UUID>.kickoff -> Crew***.kickoff (evaluate_trajectory.py handles automatically)
#    - LLM: * -> Matches any model name (e.g. gpt-4o-mini, deepseek-r1, etc.)
#
# 3. Exact match requirements:
#    - SPAN name: "GameBuilder_generation" (exact match)
#    - Chain name: "Crew***.kickoff" (wildcard match)
#    - AGENT names: Must exactly match definitions in agents.yaml
#      * "Senior Software Engineer"
#      * "Software Quality Control Engineer"
#      * "Chief Software Quality Control Engineer"
#    - LLM name: "LLM: *" (wildcard matches any model)
#    - Tool name: "Python Code Validator" (exact match)
#
# 4. Evaluation metrics meaning:
#    - Exact Match: Requires trajectory to be completely identical (including LLM call count)
#    - In-order Match: Allows extra calls, but core steps must appear in order
#    - Any-order Match: Only requires containing all necessary steps (ignores order)
#    - Precision: Proportion of correct steps in predicted trajectory
#    - Recall: Proportion of reference trajectory steps covered
#    - Single-tool Use: Detects Python Code Validator tool usage
#
# 5. Run command:
#    cd /Users/wzr/TOSEM-2025/RESULTS/RQ1/GameBuilder
#    python3 evaluate_trajectory.py --config reference_trajectory.yaml
#
# 6. Design notes:
#    - This reference trajectory represents ideal execution path
#    - Senior Engineer: 1 LLM call (generate code)
#    - QA Engineer: 2 LLM + 1 tool (review -> validate -> output)
#    - Chief QA: 2 LLM + 1 tool (evaluate -> validate -> confirm)
#    - Total: 5 LLM calls + 2 tool calls
#    - In-order Match and Any-order Match metrics are more suitable for evaluating actual performance
#
# 7. Differences from actual trajectory:
#    - Actual trajectory may include more LLM calls (thinking, planning, execution, summary, etc.)
#    - Actual trajectory may call validation tool multiple times
#    - These differences don't represent errors, but different model execution strategies
#    - Exact Match score may be low, focus on In-order Match and Recall