Semantic LOC counter that classifies code by role (core, test, docs, infra) with health ratios and effort estimates.
brew install modern-tooling/tap/alocgo install github.com/modern-tooling/aloc/cmd/aloc@latestgit clone https://github.com/modern-tooling/aloc.git
cd aloc
go build -o aloc ./cmd/alocaloc . # Analyze current directory
aloc . --effort # Include effort estimates
aloc . --format json --pretty # JSON output
aloc . --deep # Deep analysis (header probing)Codebase Scale - Total lines, files, and languages in a single line.
Responsibility Balance - How code is distributed across roles (core, test, docs, infra, config).
Language Breakdown - LOC by language, grouped by category (Primary, DevOps, Data, Documentation). Includes embedded code detection (e.g., code blocks in Markdown).
Health Ratios - Key metrics with visual gauges:
- Test / Core - test coverage relative to core code
- Comment / Code - explanation density
- Docs / Core - documentation coverage
- Infra / Core - operational complexity
- Config / Core - configuration surface area
Development Effort Models - Cost and timeline estimates using two models:
- Market Replacement (Conventional Team) - COCOMO-based estimate for traditional teams
- AI-Native Team (Agentic/Parallel) - Estimate for teams using AI-assisted parallel workflows
| Flag | Description |
|---|---|
--format, -f |
Output format: tui (default), json |
--effort |
Include effort estimates |
--git |
Enable git history analysis (churn sparklines, stability metrics) |
--git-months |
Months of history for git analysis (default: 6) |
--deep |
Enable header probing and extensionless file analysis |
--files |
Include file-level details |
--pretty |
Pretty-print JSON output |
--ai-model |
AI model for cost estimation: sonnet, opus, haiku |
--human-cost |
Monthly cost per engineer (default: 15000) |
--config, -c |
Config file path |
--no-color |
Disable colors |
--no-embedded |
Hide embedded code blocks in Markdown |
AI-assisted commits are shown as timeline markers to contextualize periods of iteration and rework.
Create aloc.yaml in your project root:
exclude:
- "**/fixtures/**"
- "**/testdata/**"
options:
header_probe: false
neighborhood: true
overrides:
test:
- "**/testing/**"
generated:
- "**/*.gen.go"| Role | Description |
|---|---|
| core | Core application code |
| test | Test files |
| infra | Infrastructure (Terraform, Docker, CI) |
| docs | Documentation |
| config | Configuration files |
| generated | Auto-generated code |
| vendor | Third-party code |
| scripts | Build scripts and tools |
| examples | Example code |
| deprecated | Deprecated code |
Optimized for large monorepos:
- Skips cache directories (.pnpm-store, .terraform, node_modules, etc.)
- Quick mode (default) scans only known source extensions
- Deep mode analyzes extensionless files and probes headers
- AI Cost Model - Token estimation methodology and pricing
aloc estimates development effort using multiple delivery models, not a single number.
This reflects a simple reality: the cost of software depends as much on how teams work as on how much code exists.
This model answers: "What would it cost to replace this codebase using a typical engineering organization?"
Assumptions:
- Conventional team structure with mixed skill distribution
- Sequential work with coordination overhead
- Partial AI usage (autocomplete, chat assistance)
Output is presented as a range, reflecting uncertainty in productivity and rework:
Market Replacement Estimate (Conventional Team)
$12.9M – $17.0M · 30–36 months · ~30 engineers
This estimate is comparable to COCOMO-style models and is useful for executive planning, acquisition diligence, and large-scale budgeting.
This model reflects a different delivery approach:
- Work is decomposed into tasks and executed in parallel
- Multiple AI agents draft code, tests, and documentation
- Humans supervise, review, integrate, and make final decisions
- Coordination overhead is reduced, but not eliminated
This model does not assume replacement of human judgment. Instead, it reflects AI-assisted parallel execution under human oversight.
AI-Native Team Estimate (Agentic/Parallel)
~$6.5M – $9.5M · 12–18 months · ~10–15 engineers
(AI tooling: ~$2K–$10K per month)
This estimate is intentionally conservative and should be interpreted as:
- Feasible for teams that embrace agentic workflows
- Dependent on effective task decomposition
- Not a universal outcome
These estimates are best-effort approximations for rough relative comparison. The field would benefit from more rigorous research on development effort analysis—both for conventional teams and AI-native workflows. If you're aware of relevant studies or have data to share, contributions are welcome.
MIT