aprender-serve 0.34.0

Pure Rust ML inference engine built from scratch - model serving for GGUF and safetensors
# PMAT Configuration for Realizar
# Quality gates and technical debt grading

[quality]
# Minimum acceptable coverage percentage
min_coverage = 85.0

# Maximum cyclomatic complexity per function
max_complexity = 10

# Minimum mutation score percentage
mutation_score = 80.0

# Maximum allowed TODOs
max_todos = 5

[tdg]
# Technical Debt Grading (TDG) thresholds
# A: 90-100, B: 80-89, C: 70-79, D: 60-69, F: <60

# Coverage weight in TDG score
coverage_weight = 0.3

# Complexity weight in TDG score
complexity_weight = 0.2

# Test count weight in TDG score
test_weight = 0.2

# Documentation weight in TDG score
doc_weight = 0.15

# Mutation score weight in TDG score
mutation_weight = 0.15

[roadmap]
# Sprint configuration
current_sprint = 1
sprint_length_weeks = 2
velocity = 15  # story points per sprint

# Milestone tracking
milestones = [
    { name = "Phase 1: Foundation", target_sprint = 4 },
    { name = "Phase 2: GPU Acceleration", target_sprint = 8 },
    { name = "Phase 3: WASM Support", target_sprint = 12 },
    { name = "Phase 4: Advanced Operations", target_sprint = 16 },
    { name = "Phase 5: Aprender Integration", target_sprint = 20 },
    { name = "Phase 6: Production Deployment", target_sprint = 24 },
]

[analysis]
# Paths to analyze
include = ["src/**/*.rs"]
exclude = ["target/**", "tests/**"]

# Analysis options
check_todos = true
check_fixmes = true
check_deprecated = true