How do you know if code is production-ready before you deploy it? Not by running it—by reading it. But reading code to evaluate quality is time-consuming and subjective. What makes one codebase “clean” and another “messy”? What signals indicate a project will be maintainable six months from now versus one that will accumulate technical debt?
We needed answers to these questions, so we did what you probably do when evaluating a new project: we opened the codebase and started reading. Not just one or two projects—we went through dozens of repositories known for being stable, well-maintained, and easy to work with in production. Projects that engineering teams actually deploy and keep running for years.
After reviewing approximately fifty projects, patterns began to emerge. The differences weren’t random. Well-engineered projects shared specific characteristics that you could identify just by looking at their structure and setup. That realization led us to build an automated scoring system that could evaluate technical quality at scale.
The Patterns That Kept Appearing
The first thing we noticed was the directory structure. Almost every high-quality project had a clear separation: a src/ or lib/ directory for source code, a tests/ or test/ directory for test files, and often a docs/ folder for documentation. This wasn’t just an organizational preference—it signaled that the maintainers had considered how developers would navigate and contribute to the codebase.
Payload exemplifies this pattern. Clean directory structure, clear module boundaries, tests separated from implementation code. When you clone the repo, you immediately understand where everything lives. Compare that to projects where source files, tests, config files, and documentation are all mixed together in the root directory. The second approach might work for solo developers, but it creates friction for teams and contributors.
Test coverage was the next major signal. We didn’t just count test files—we looked at the ratio of test files to source files. Projects with 10% or more test files consistently scored higher on our internal quality assessments. Why? Because teams that invest in testing usually invest in other quality practices as well. Mastodon maintains comprehensive test coverage across its codebase, which correlates with its stability and successful scaling to millions of users.
Type safety emerged as another strong indicator. Whether it was TypeScript for JavaScript projects or type hints in Python codebases, the presence of static typing told us the team cared about catching errors before runtime. TypeSense uses TypeScript extensively in its tooling layer, providing safety guarantees that make the codebase easier to maintain and refactor. Projects without type safety weren’t automatically bad, but the pattern was clear: type-safe projects had fewer bugs in production.
CI/CD configuration files were everywhere in high-quality projects. GitHub Actions workflows, GitLab CI configs, or Jenkins setups—the specific tool didn’t matter. What mattered was that the team had automated their quality gates. When you see .github/workflows/ with test runners, linters, and build checks, you know that changes go through validation before merging. Directus runs automated tests on every pull request, catching issues before they reach the main branch.
We also observed that the best projects consistently used linters and formatters. ESLint, Prettier, Ruff, Black—seeing these config files meant the codebase followed consistent style rules. Not because consistency is aesthetically pleasing (though it is), but because it reduces cognitive load when reading code. LibreChat enforces code style through automated tooling, so every file looks as if it were written by the same person, even with dozens of contributors.
Building the Scoring System
Once we identified these patterns, we built automated detection for each signal. The system scans codebases and evaluates four dimensions:
Code Quality carries the most weight, accounting for 30% of the overall score. This includes test coverage ratio (projects with 10%+ test coverage receive the maximum points here), linter configuration, formatter setup, CI/CD pipelines, type safety via TypeScript or type hints, and error-handling patterns. A project maxes out code quality when it has comprehensive tests, automated tooling, continuous integration, strong typing, and thoughtful error handling throughout.
Architecture also accounts for 30%, with a focus on structure and organization. We assess the directory layout, module separation, use of design patterns (classes, components, and clear routing structures), and the logical categorization of files. The sweet spot for top-level directories turns out to be 5-15—fewer than that, and everything might be crammed together; more than that, and navigation becomes difficult. Swagger UI excels in its architecture, with a plugin-based system and clear module boundaries.
Documentation accounts for 20% of the score. This includes API documentation using JSDoc or docstrings, inline comments that explain complex logic, type annotations that serve as self-documentation, and dedicated docs directories. Projects with 100+ documented functions or classes get maximum points. CapRover maintains extensive documentation that makes it approachable despite its deployment complexity.
Complexity accounts for the final 20%, but it’s inverted—lower complexity yields a higher score. We measure codebase size (fewer files are better), average file complexity (smaller files are easier to understand), and dependency count (fewer dependencies mean less to learn). This is where small, focused projects can outshine large enterprise codebases. A project with 50 well-organized files and 10 dependencies is often easier to work with than one with 1,000 files and 100 dependencies, even if the larger one has more features.
The overall score is computed as a weighted average: Code Quality × 30% + Architecture × 30% + Documentation × 20% + Complexity × 20%. Projects like Payload score in the low 80s range, indicating production-ready code with solid practices across all dimensions.
What the Scores Reveal
When we see a technical score around 80, we’re looking at a codebase that checks most of the boxes for production deployment. These projects have automated testing, clear structure, good documentation, and reasonable complexity. You can clone the repo, understand the architecture in an hour or two, and contribute without stepping on landmines. Projects like Mastodon and TypeSense demonstrate these patterns, with scores in the high 70s to low 80s—mature, well-engineered projects that teams deploy with confidence.
Scores of 60-79 indicate solid codebases that may lack some quality tooling but remain maintainable. They might have decent test coverage but no CI/CD, or good structure but limited documentation. Directus falls at the upper end of this range, in the high 70s, showing strong architecture and code quality, with room for improvement in complexity management. These projects work in production; you may need to add tooling or documentation as you scale.
When scores fall between 40 and 59, we’re seeing functional code with rough edges. Maybe there’s minimal testing, or the directory structure is disorganized, or complexity is high due to many dependencies and large files. These projects might work well for specific use cases, but you’ll probably need to invest time in cleanup to maintain them long-term.
A score below 40 usually indicates either a very new experimental project that hasn’t yet built out quality infrastructure or an older project that needs significant cleanup. Not unusable, but definitely higher risk for production deployments.
The Surprises
We expected large, popular projects to score the highest. That’s not what happened. Some projects with tens of thousands of stars scored in the 60s because they had grown organically without strong architectural boundaries or comprehensive testing. Meanwhile, smaller projects with a few thousand stars sometimes scored in the 80s because they were built with quality practices from day one.
Forem taught us this lesson. Despite its large codebase powering major community platforms, it maintains high scores in the upper 70s through rigorous testing and clear architectural patterns. Size doesn’t automatically mean complexity if the structure is sound.
We also learned that the test coverage ratio is one of the single best predictors of overall code quality. Projects with 15% or more of their files dedicated to tests almost always had other quality practices in place: CI/CD, linting, and good documentation. Teams that prioritize testing tend to prioritize other engineering excellence practices too.
Type safety turned out to be more significant than we initially thought. TypeScript projects consistently scored 10-15 points higher in code quality than equivalent JavaScript projects without types. The same pattern held for Python projects with extensive type hints versus those without. Type safety isn’t just about catching bugs—it’s a signal that the team thinks systematically about their codebase.
The directory structure mattered more than we expected. Projects with clear src/, tests/, and docs/ separation scored significantly higher on architecture than projects where everything lived in the root or in poorly named directories. This makes sense in hindsight: how you organize code reflects how you think about its structure and boundaries.
The complexity dimension revealed that some of the best codebases are actually quite small. SendPortal demonstrates this: a focused scope, a manageable codebase, and clear Laravel conventions. Sometimes the best engineering decision is knowing what NOT to build.
Using Scores in Practice
When you see a technical score on Open Apps, you’re seeing the output of this automated analysis. We scan the entire codebase, count patterns, detect tooling configurations, and calculate weighted scores across those four dimensions.
For production deployments where reliability matters, we recommend filtering for scores above 75. These codebases have demonstrated a commitment to quality practices. For side projects or internal tools where you have more tolerance for rough edges, scores above 60 are usually fine. You’re accepting some technical debt in exchange for faster implementation or specific features.
The score won’t tell you if a project fits your exact use case. It won’t tell you whether the API design meets your needs or whether the feature set is complete. But it will tell you whether the codebase is well-engineered, maintainable, and likely to remain so as it evolves. That’s information you can’t easily get from stars, commits, or README files.
When we started this project, we thought we’d need machine learning or complex heuristics to evaluate code quality. Turns out, the patterns that indicate excellent engineering are surprisingly consistent and detectable through straightforward analysis. Good projects have tests, clear structure, documentation, type safety, and automated checks. Bad projects don’t. The correlation is that simple.
Every week, we run these scans on new projects added to Open Apps. Every week, the scores help developers identify which open source tools are built to last.