Project 11: Conventional Commits Enforcer — Automate Semantic Versioning

A commit message linter that enforces the Conventional Commits specification, generates changelogs automatically, and determines semantic version bumps based on commit types.

Quick Reference

Attribute Value
Difficulty Intermediate
Time Estimate 1 week
Main Programming Language Rust
Alternative Programming Languages Go, Python, TypeScript
Coolness Level Level 3: Genuinely Clever
Business Potential 2. The “Micro-SaaS / Pro Tool”
Prerequisites Understanding of regex, basic parsing concepts
Key Topics Conventional Commits Specification, Semantic Versioning, Changelog Best Practices

1. Learning Objectives

By completing this project, you will:

  1. Implement a working version of: A commit message linter that enforces the Conventional Commits specification, generates changelogs automatically, and determines semantic version bumps based on commit types..
  2. Explain the core Git workflow tradeoff this project is designed to surface.
  3. Design deterministic checks so results can be verified and reproduced.
  4. Document operational failure modes and safe recovery actions.

2. All Theory Needed (Per-Concept Breakdown)

Conventional Commits Specification

Fundamentals This concept matters in this project because your implementation will fail or become non-deterministic without a precise model of Conventional Commits Specification. You should define what the concept controls, what invariants must hold, and which actions are safe versus destructive. Treat this concept as a production concern, not a tutorial checkbox.

Deep Dive into the concept When applying Conventional Commits Specification in this project, reason in three passes: data shape, state transitions, and enforcement. First, identify which artifacts are authoritative (commit objects, refs, metadata, policy config, CI status, or scan findings). Second, map how those artifacts change when your tool runs. Third, define failure behavior explicitly. In Git tooling, silent partial success is dangerous: you need either complete success with evidence or an explicit failure state with remediation guidance. Also account for scale behavior. A workflow that works on a toy repo may fail on large history depth, concurrent updates, or mixed branch policies. Include trace logs for every irreversible action, and separate simulation mode from write mode. For interview readiness, be able to explain how this concept protects delivery speed while reducing operational risk.

How this fit on projects In this project, Conventional Commits Specification is directly used in design decisions, implementation constraints, and verification criteria.

Definitions & key terms

  • Conventional Commits Specification invariant: A condition that must remain true before and after every operation.
  • Safety boundary: The point where actions become destructive unless guarded.
  • Verification signal: Evidence proving the action behaved as expected.

Mental model diagram

Input state -> Validate invariant -> Apply change -> Verify output -> Record evidence

How it works

  1. Capture current state and constraints.
  2. Evaluate whether Conventional Commits Specification preconditions are satisfied.
  3. Execute the minimal safe transition.
  4. Verify postconditions and publish an auditable result.

Failure modes: stale state, partial writes, race conditions, ambiguous output contracts.

Minimal concrete example

Plan -> dry-run -> execute -> verify -> rollback/forward-fix decision

Common misconceptions

  • Assuming local success implies team-safe behavior.
  • Treating policy violations as warnings instead of merge blockers.
  • Skipping deterministic verification because the output appears correct.

Check-your-understanding questions

  1. Which invariant is most likely to break first under concurrency?
  2. What output proves your tool handled an edge case correctly?
  3. Where should enforcement happen: local hook, CI, or protected branch gate?

Check-your-understanding answers

  1. The invariant tied to mutable refs or policy-dependent merge eligibility.
  2. A deterministic transcript showing both success and controlled failure behavior.
  3. Layered enforcement: fast local checks plus non-bypassable server-side gates.

Real-world applications

  • Change-management tooling for fast-moving teams.
  • Incident-safe release workflows with traceable rollback paths.
  • Compliance-ready source-control automation.

Where you’ll apply it This project and its immediate adjacent projects in this sprint.

References

  • https://git-scm.com/docs
  • https://dora.dev/capabilities/trunk-based-development/

Key insights Conventional Commits Specification is only valuable when its invariants are encoded into tooling and checks.

Summary Mastering Conventional Commits Specification here gives you transferable patterns for larger workflow systems.

Homework/Exercises to practice the concept

  1. Write one failing scenario and expected detection output.
  2. Define one invariant and one explicit violation test.

Solutions to the homework/exercises

  1. Use a stale branch or invalid metadata case and assert deterministic error reporting.
  2. Invariant: protected branch must not accept unchecked changes; violation test: bypass attempt should fail fast.

Semantic Versioning

Fundamentals This concept matters in this project because your implementation will fail or become non-deterministic without a precise model of Semantic Versioning. You should define what the concept controls, what invariants must hold, and which actions are safe versus destructive. Treat this concept as a production concern, not a tutorial checkbox.

Deep Dive into the concept When applying Semantic Versioning in this project, reason in three passes: data shape, state transitions, and enforcement. First, identify which artifacts are authoritative (commit objects, refs, metadata, policy config, CI status, or scan findings). Second, map how those artifacts change when your tool runs. Third, define failure behavior explicitly. In Git tooling, silent partial success is dangerous: you need either complete success with evidence or an explicit failure state with remediation guidance. Also account for scale behavior. A workflow that works on a toy repo may fail on large history depth, concurrent updates, or mixed branch policies. Include trace logs for every irreversible action, and separate simulation mode from write mode. For interview readiness, be able to explain how this concept protects delivery speed while reducing operational risk.

How this fit on projects In this project, Semantic Versioning is directly used in design decisions, implementation constraints, and verification criteria.

Definitions & key terms

  • Semantic Versioning invariant: A condition that must remain true before and after every operation.
  • Safety boundary: The point where actions become destructive unless guarded.
  • Verification signal: Evidence proving the action behaved as expected.

Mental model diagram

Input state -> Validate invariant -> Apply change -> Verify output -> Record evidence

How it works

  1. Capture current state and constraints.
  2. Evaluate whether Semantic Versioning preconditions are satisfied.
  3. Execute the minimal safe transition.
  4. Verify postconditions and publish an auditable result.

Failure modes: stale state, partial writes, race conditions, ambiguous output contracts.

Minimal concrete example

Plan -> dry-run -> execute -> verify -> rollback/forward-fix decision

Common misconceptions

  • Assuming local success implies team-safe behavior.
  • Treating policy violations as warnings instead of merge blockers.
  • Skipping deterministic verification because the output appears correct.

Check-your-understanding questions

  1. Which invariant is most likely to break first under concurrency?
  2. What output proves your tool handled an edge case correctly?
  3. Where should enforcement happen: local hook, CI, or protected branch gate?

Check-your-understanding answers

  1. The invariant tied to mutable refs or policy-dependent merge eligibility.
  2. A deterministic transcript showing both success and controlled failure behavior.
  3. Layered enforcement: fast local checks plus non-bypassable server-side gates.

Real-world applications

  • Change-management tooling for fast-moving teams.
  • Incident-safe release workflows with traceable rollback paths.
  • Compliance-ready source-control automation.

Where you’ll apply it This project and its immediate adjacent projects in this sprint.

References

  • https://git-scm.com/docs
  • https://dora.dev/capabilities/trunk-based-development/

Key insights Semantic Versioning is only valuable when its invariants are encoded into tooling and checks.

Summary Mastering Semantic Versioning here gives you transferable patterns for larger workflow systems.

Homework/Exercises to practice the concept

  1. Write one failing scenario and expected detection output.
  2. Define one invariant and one explicit violation test.

Solutions to the homework/exercises

  1. Use a stale branch or invalid metadata case and assert deterministic error reporting.
  2. Invariant: protected branch must not accept unchecked changes; violation test: bypass attempt should fail fast.

Changelog Best Practices

Fundamentals This concept matters in this project because your implementation will fail or become non-deterministic without a precise model of Changelog Best Practices. You should define what the concept controls, what invariants must hold, and which actions are safe versus destructive. Treat this concept as a production concern, not a tutorial checkbox.

Deep Dive into the concept When applying Changelog Best Practices in this project, reason in three passes: data shape, state transitions, and enforcement. First, identify which artifacts are authoritative (commit objects, refs, metadata, policy config, CI status, or scan findings). Second, map how those artifacts change when your tool runs. Third, define failure behavior explicitly. In Git tooling, silent partial success is dangerous: you need either complete success with evidence or an explicit failure state with remediation guidance. Also account for scale behavior. A workflow that works on a toy repo may fail on large history depth, concurrent updates, or mixed branch policies. Include trace logs for every irreversible action, and separate simulation mode from write mode. For interview readiness, be able to explain how this concept protects delivery speed while reducing operational risk.

How this fit on projects In this project, Changelog Best Practices is directly used in design decisions, implementation constraints, and verification criteria.

Definitions & key terms

  • Changelog Best Practices invariant: A condition that must remain true before and after every operation.
  • Safety boundary: The point where actions become destructive unless guarded.
  • Verification signal: Evidence proving the action behaved as expected.

Mental model diagram

Input state -> Validate invariant -> Apply change -> Verify output -> Record evidence

How it works

  1. Capture current state and constraints.
  2. Evaluate whether Changelog Best Practices preconditions are satisfied.
  3. Execute the minimal safe transition.
  4. Verify postconditions and publish an auditable result.

Failure modes: stale state, partial writes, race conditions, ambiguous output contracts.

Minimal concrete example

Plan -> dry-run -> execute -> verify -> rollback/forward-fix decision

Common misconceptions

  • Assuming local success implies team-safe behavior.
  • Treating policy violations as warnings instead of merge blockers.
  • Skipping deterministic verification because the output appears correct.

Check-your-understanding questions

  1. Which invariant is most likely to break first under concurrency?
  2. What output proves your tool handled an edge case correctly?
  3. Where should enforcement happen: local hook, CI, or protected branch gate?

Check-your-understanding answers

  1. The invariant tied to mutable refs or policy-dependent merge eligibility.
  2. A deterministic transcript showing both success and controlled failure behavior.
  3. Layered enforcement: fast local checks plus non-bypassable server-side gates.

Real-world applications

  • Change-management tooling for fast-moving teams.
  • Incident-safe release workflows with traceable rollback paths.
  • Compliance-ready source-control automation.

Where you’ll apply it This project and its immediate adjacent projects in this sprint.

References

  • https://git-scm.com/docs
  • https://dora.dev/capabilities/trunk-based-development/

Key insights Changelog Best Practices is only valuable when its invariants are encoded into tooling and checks.

Summary Mastering Changelog Best Practices here gives you transferable patterns for larger workflow systems.

Homework/Exercises to practice the concept

  1. Write one failing scenario and expected detection output.
  2. Define one invariant and one explicit violation test.

Solutions to the homework/exercises

  1. Use a stale branch or invalid metadata case and assert deterministic error reporting.
  2. Invariant: protected branch must not accept unchecked changes; violation test: bypass attempt should fail fast.

3. Project Specification

3.1 What You Will Build

A commit message linter that enforces the Conventional Commits specification, generates changelogs automatically, and determines semantic version bumps based on commit types.

3.2 Functional Requirements

  1. Scope control: Deliver a deterministic and testable implementation.
  2. Correctness: Preserve Git invariants and policy constraints.

3.3 Non-Functional Requirements

  • Performance: Deterministic execution with documented runtime behavior on representative history sizes.
  • Reliability: Repeated runs on the same input produce identical outputs.
  • Usability: Clear CLI or report output for both success and failure cases.

3.4 Example Usage / Output

You’ll have a complete commit linting and changelog system:

Example Output:

$ commit-lint "Add new feature"
❌ Invalid commit message

Error: Missing type prefix
Expected format: <type>[optional scope]: <description>

Valid types: feat, fix, docs, style, refactor, test, chore
Example: "feat: add new feature"

$ commit-lint "feat: add user authentication"
✓ Valid commit message

Type: feat (minor version bump)
Scope: none
Description: add user authentication
Breaking: no

$ commit-lint "fix(auth)!: handle token expiration correctly"
✓ Valid commit message

Type: fix (patch version bump)
Scope: auth
Description: handle token expiration correctly
Breaking: YES (major version bump)

$ changelog generate --from v1.2.0 --to HEAD

# Changelog

### 3.5 Data Formats / Schemas / Protocols

Describe input repository assumptions, output report shape, and any policy/config schema consumed by the tool.

### 3.6 Edge Cases

- Empty repository or shallow clone state.
- Detached HEAD or rewritten history during execution.
- Invalid metadata/policy configuration.

### 3.7 Real World Outcome

You'll have a complete commit linting and changelog system:

**Example Output:**
```bash
$ commit-lint "Add new feature"
❌ Invalid commit message

Error: Missing type prefix
Expected format: <type>[optional scope]: <description>

Valid types: feat, fix, docs, style, refactor, test, chore
Example: "feat: add new feature"

$ commit-lint "feat: add user authentication"
✓ Valid commit message

Type: feat (minor version bump)
Scope: none
Description: add user authentication
Breaking: no

$ commit-lint "fix(auth)!: handle token expiration correctly"
✓ Valid commit message

Type: fix (patch version bump)
Scope: auth
Description: handle token expiration correctly
Breaking: YES (major version bump)

$ changelog generate --from v1.2.0 --to HEAD

# Changelog

---

## 4. Solution Architecture

### 4.1 High-Level Design

```text
Inputs -> Validation -> Core Engine -> Output Formatter -> Verification Report

4.2 Key Components

Component Responsibility Key Decisions
Input loader Discover commits/refs/config inputs Deterministic ordering and clear failure messages
Core engine Compute project-specific logic Separate read-only simulation from mutating actions
Reporter Produce user-facing output and evidence Include machine-readable and human-readable forms

4.4 Data Structures (No Full Code)

ProjectState { refs, commits, policy, findings, metrics }
Result { status, evidence, warnings, next_actions }

4.4 Algorithm Overview

  1. Collect state from repository and configuration.
  2. Evaluate invariants and policy preconditions.
  3. Execute core transformation or analysis logic.
  4. Verify postconditions and emit deterministic report.

Complexity Analysis:

  • Time: O(history + affected scope)
  • Space: O(active graph window + report size)

5. Implementation Guide

5.1 Development Environment Setup

Use the environment defined in the main guide. Pin tool versions and fixture data to keep outputs reproducible.

5.2 Project Structure

project-root/
├── fixtures/
├── src/
├── tests/
├── docs/
└── README.md

5.3 The Core Question You’re Answering

“How do you turn commit messages into automated releases and changelogs?”

Before you write any code, sit with this question. The answer is conventions. When every commit follows a pattern, machines can parse them to determine what changed, how to version it, and what to tell users.

5.4 Concepts You Must Understand First

Stop and research these before coding:

  1. Conventional Commits Specification
    • What are the required elements (type, description)?
    • What are the optional elements (scope, body, footer)?
    • How do you indicate breaking changes?
    • Resource: conventionalcommits.org
  2. Semantic Versioning
    • What do MAJOR.MINOR.PATCH mean?
    • When do you bump each number?
    • What’s a pre-release version?
    • Resource: semver.org
  3. Changelog Best Practices
    • What sections should a changelog have?
    • How do you group changes by type?
    • What makes a changelog human-readable?
    • Resource: keepachangelog.com

5.5 Questions to Guide Your Design

Before implementing, think through these:

  1. Parsing
    • How do you handle multi-line commit messages?
    • How do you extract the body vs. footers?
    • What regex pattern matches the Conventional Commits format?
  2. Version Determination
    • What if there are multiple breaking changes?
    • How do you handle version ranges (e.g., v1.0.0 to v2.0.0)?
    • What about pre-release versions?
  3. Integration
    • How do you make this work as a commit-msg hook?
    • How do you handle commits that bypass hooks?
    • Should CI also validate commit messages?

5.6 Thinking Exercise

Parse Example Commits

Analyze these commit messages:

1. feat(auth): add OAuth2 support
2. fix: resolve memory leak in worker pool
3. feat!: redesign API response format
4. docs(readme): update installation instructions
5. chore(deps): bump lodash from 4.17.20 to 4.17.21
6. refactor(core): extract validation logic

   This change moves validation into a separate module
   for better testability.

   BREAKING CHANGE: ValidationError now includes error codes
   Reviewed-by: Alice
   Refs: #123

Questions while parsing:

  • What’s the type, scope, and description for each?
  • Which commits bump which version component?
  • How do you extract the body from commit 6?
  • How do you detect the BREAKING CHANGE footer?

5.7 The Interview Questions They’ll Ask

Prepare to answer these:

  1. “Why are conventional commits useful for automated releases?”
  2. “How would you parse a commit message to extract the type and scope?”
  3. “Explain semantic versioning and when you’d bump each number.”
  4. “How would you handle enforcing commit conventions in a large team?”
  5. “What’s the difference between a breaking change in the type (!) and in the footer?”

5.8 Hints in Layers

Hint 1: Starting Point The basic regex: ^(\w+)(\(.+\))?!?: (.+)$. This captures type, optional scope, and description.

Hint 2: Body and Footers Split on double newlines. First paragraph is the subject. Remaining paragraphs are body unless they match key: value or BREAKING CHANGE:.

Hint 3: Version Bump Logic BREAKING CHANGE or ! → major. feat → minor. fix → patch. Everything else → no bump.

Hint 4: Changelog Grouping Use a map: {feat: [], fix: [], docs: []}. Iterate commits, append to appropriate bucket, then render.

5.9 Books That Will Help

Topic Book Chapter
Git hooks “Pro Git” by Chacon Ch. 8.3
Regex parsing “Mastering Regular Expressions” by Friedl Ch. 2-3
Release automation “Continuous Delivery” by Humble & Farley Ch. 5

5.10 Implementation Phases

Phase 1: Foundation (1-2 sessions)

  • Define fixtures, expected outputs, and invariant checks.
  • Build read-only analysis path.

Phase 2: Core Functionality (2-4 sessions)

  • Implement project-specific core logic and deterministic reporting.
  • Add policy and edge-case handling.

Phase 3: Polish and Edge Cases (1-2 sessions)

  • Add failure demos, performance notes, and usability improvements.
  • Finalize docs and validation transcripts.

5.11 Key Implementation Decisions

Decision Options Recommendation Rationale
Execution mode direct write vs dry-run+write dry-run+write Safer and easier to debug
Output contract free text vs structured+text structured+text Better automation and readability
Enforcement location local only vs local+CI local+CI Prevents bypass in shared branches

6. Testing Strategy

6.1 Test Categories

  • Unit tests for parsing and policy logic.
  • Integration tests on fixture repositories.
  • Edge-case tests for stale refs, malformed metadata, and large histories.

6.2 Critical Test Cases

  1. Deterministic golden-path scenario.
  2. Policy violation hard-fail scenario.
  3. Recovery path after partial or conflicting state.

6.3 Test Data

Use fixed repository fixtures with known commit graphs and expected outputs stored under version control.

7. Common Pitfalls & Debugging

Problem 1: “Output looks correct but history or metadata is inconsistent”

  • Why: Validation happens after mutation, not before.
  • Fix: Add a preflight invariant check and a post-write verification step.
  • Quick test: Run the same command twice on the same fixture and verify identical results.

Problem 2: “Tool works on small repo but times out on larger history”

  • Why: Full traversal is performed where selective traversal is possible.
  • Fix: Cache intermediate graph lookups and scope analysis to affected commits/paths.
  • Quick test: Compare runtime on small and large fixtures with a clear budget target.

Problem 3: “Policy check can be bypassed by local-only behavior”

  • Why: Enforcement is advisory, not server-authoritative.
  • Fix: Mirror critical checks in CI and protected branch rules.
  • Quick test: Attempt merge with failing policy in CI and confirm hard block.

8. Extensions & Challenges

8.1 Beginner Extensions

  • Add richer error messages with remediation hints.
  • Add fixture generation helpers for repeatable demos.

8.2 Intermediate Extensions

  • Add performance instrumentation and budget assertions.
  • Add policy configuration profiles by repository type.

8.3 Advanced Extensions

  • Add distributed execution support for large repositories.
  • Add signed evidence exports for compliance workflows.

9. Real-World Connections

9.1 Industry Applications

  • Internal developer portals.
  • Enterprise repository governance systems.
  • Release safety and incident diagnostics tooling.
  • Git core: https://git-scm.com/
  • GitHub CLI: https://github.com/cli/cli
  • pre-commit framework: https://pre-commit.com/

9.3 Interview Relevance

This project prepares you for architecture and debugging interviews that focus on merge policy, CI gates, and workflow reliability tradeoffs.

10. Resources

10.1 Essential Reading

  • Pro Git (Internals and Workflows chapters)
  • Software Engineering at Google (Version control and build chapters)
  • Accelerate (delivery performance practices)

10.2 Video Resources

  • Git internals talks from Git Merge conference archives.
  • DORA and delivery metrics conference sessions.

10.3 Tools and Documentation

  • https://git-scm.com/docs
  • https://docs.github.com/
  • https://dora.dev/

11. Self-Assessment Checklist

11.1 Understanding

  • I can explain the primary invariant this project enforces.
  • I can explain one failure mode and one safe recovery path.

11.2 Implementation

  • Functional requirements are met on deterministic fixtures.
  • Critical edge cases are tested and documented.

11.3 Growth

  • I can describe tradeoffs in an interview setting.
  • I documented what I would change in a production version.

12. Submission / Completion Criteria

Minimum Viable Completion:

  • Deterministic golden-path output exists.
  • One failure scenario is handled with clear output.
  • Core workflow objective is demonstrably met.

Full Completion:

  • Minimum criteria plus policy validation, structured reporting, and edge-case coverage.

Excellence:

  • Full completion plus measurable performance budget and production-hardening notes.