A hybrid approach combining deterministic rules-based tooling with context-aware AI agents to automate everything that can be automated about code review.
It should be noted that this solution is for teams and organizations that are already have a solid continuous delivery process. For teams still on that journey, do not start here! Start with a vision of here. Please read my article on how to get there faster with the assistance of agents.
| #!/usr/bin/env python3 | |
| """ | |
| Test Quality Reviewer | |
| Analyzes test code for quality, coverage, maintainability, and best practices. | |
| Usage: | |
| python test-quality-review.py <test_file_or_directory> [--output report.md] | |
| python test-quality-review.py tests/ --output test-quality-report.md | |
| """ |
| Aspect | Open Source | Enterprise |
|---|---|---|
| Governance | Maintainers, community consensus, meritocracy-based influence | Empowered teams, lightweight oversight, guardrails over gates |
| Code Review | Public PRs, async reviews from global contributors, emphasis on transparency | Small PRs, fast feedback loops, pair/mob programming as alternative |
| Documentation | README-driven, contributor guides essential, public wikis | Living documentation, lightweight ADRs, docs as code |
| Branching Strategy | Fork-and-PR model, contributors work in personal forks | Trunk-based development, short-lived feature branches (< 1 day) |
| Communication | Public channels (GitHub issues, Discord, mailing lists) | Co-located teams (in space /and or time), minimal ceremony |
| Release Cadence | Varies widely, often semver-based, maintainer-driven | Continuous delivery, deploy on merge, feature flags for controlled rollout |
| Testing | CI on PRs, community-contributed |
Feature: I want to be able to add items to a shopping cart so that I can purchase them
Scenario: Adding a new item to an empty cart
Given the cart is empty
When I add item 123 to the cart
Then the cart should contain item 123
And the quantity should be 1
Scenario: Increasing quantity for an item already in the cart
graph TD
Start[New Code Change] --> Q1{Is this a large or<br/>high-risk change?}
Q1 -->|Yes| Q2{Do you need gradual<br/>rollout or testing<br/>in production?}
Q1 -->|No| Q3{Is the feature<br/>incomplete or spans<br/>multiple releases?}
Q2 -->|Yes| UseFF1[✓ USE FEATURE FLAG<br/>Enables safe rollout<br/>and quick rollback]
| Manufacturing | Development |
|---|---|
| Each item is identical | Each delivery is unique |
| The value of the item is known | The value of the change is uncertain |
| The effort to build the item is known | The effort to develop is uncertain |
| The raw materials have value | The raw materials have no value |
# Perform a similarity search between the embedding of the query and the embeddings of the documents
query = "How big should a story be?"
results = vector_search.similarity_search(query)
print(results[0].page_content)Stories should be sliced vertically. That is, the story should be aligned such that it fulfills a consumer request