Code Quality Tools Beyond Linting

[cleder]

The landscape of Python software quality tooling is currently defined by two contrasting forces: high-velocity convergence and deep specialization. The recent, rapid adoption of Ruff has solved the long-standing community problem of coordinating dozens of separate linters and formatters, establishing a unified, high-performance axis for standard code quality.

A second category of tools continues to operate in necessary, but isolated, silos. Tools dedicated to architectural enforcement and deep structural metrics, such as:

• import-linter (Layered architecture enforcement)
• tach (Dependency visualization and enforcement)
• complexipy, radon, lizard (Metrics for overall and cognitive complexity)
• module_coupling_metrics, lcom, and cohesion (Metrics for coupling and class cohesion)

These projects address fundamental challenges of code maintainability, evolvability, and architectural debt that extend beyond the scope of fast, stylistic linting. The success of Ruff now presents the opportunity to foster a cross-tool discussion focused not just on syntax, but on structure.

Specialized quality tools are vital for long-term maintainability and risk assessment. Tools like import-linter and tach mitigate technical risk by enforcing architectural rules, preventing systemic decay, and reducing change costs. Complexity and cohesion metrics from tools such as complexipy, lcom, and cohesion quantitatively flag overly complex or highly coupled components, acting as early warning systems for technical debt. By analysing the combined outputs, risk assessment shifts to predictive modelling: integrating data from individual tools (e.g., import-linter violations, complexipy scores) creates a multi-dimensional risk score. Overlaying these results, such as identifying modules that are both low in cohesion and involved in tach-flagged dependency cycles, generates a "heat map" of technical debt. This unified approach, empirically validated against historical project data like bug frequency and commit rates can yield a predictive risk assessment. It identifies modules that are not just theoretically complex but empirically confirmed sources of instability, transforming abstract quality metrics into concrete, prioritized refactoring tasks for the riskiest codebase components.

Reasons to Connect

Bring the maintainers and core users of these diverse tools into a shared discussion.

Increasing Tool Visibility and Sustainability: Specialized tools often rely on small, dedicated contributor pools and suffer from knowledge isolation, confining technical debate to their specific GitHub repository. A broader discussion provides these projects with critical outreach, exposure to a wider user base, and a stronger pipeline of new contributors, ensuring their long-term sustainability.

Let's start the conversation on how to 'measure' maintainable, and architecturally sound Python code.

And keep Goodhart's law: "When a measure becomes a target, it ceases to be a good measure" in mind ;-)

[gwdekker]

Interesting! In my python monorepo we use tach but looking for an alternative as it is no longer maintained. In addition we use polylith (https://davidvujic.github.io/python-polylith-docs/) tooling, and have some unit tests on top of some of the polylith results.

Regarding some of the tooling you mention above, I am thinking that a great first start for some use cases could maybe be to get ruff analyze graph stable (its still experimental) - seems like constructing that dependency tree could be a very nice building block for some of the tooling mentioned above to start using.

[gwdekker]

related: #20701

[CoderJoshDK]

Recently, the basedpyright maintainer made their own fork that they will supposedly be maintaining.
https://github.com/detachhead/dtach

[cleder]

I haven't heard of polylith before, it looks 😍

Just after I discovered tach and was excited about it, it became unmaintained 😢

ruff analyze graph looks promising.

[cleder]

Here is another one, only a couple of months old:
pyscn - Python Code Quality Analyzer

🔍 CFG-based dead code detection – Find unreachable code after exhaustive if-elif-else chains
📋 Clone detection with APTED + LSH – Identify refactoring opportunities with tree edit distance
🔗 Coupling metrics (CBO) – Track architecture quality and module dependencies
📊 Cyclomatic complexity analysis – Spot functions that need breaking down
100,000+ lines/sec • Built with Go + tree-sitter

[seddonym]

Hi there, thanks for starting the thread. I'm the maintainer of Import Linter and Grimp (a Rust-backed library which it uses for the heavy lifting). Also Impulse, a little library for visualizing the graph.

[allenanswerzq]

would love to see and learn how your projets work internally

[ntBre]

Yes, thank you for opening the discussion! I don't have much to add at the moment, but I'm excited to follow the thread and take a look at the tools mentioned 😄

[cleder]

https://github.com/allenanswerzq/llmcc
llmcc explores automated context generation through symbolic graph analysis.
To give you a high level architecture design for a project build a project level graph using page-rank pick the most important nodes, or find all depends on a symbol under a specific folder,
It bridges the semantic gap between human-written code/documents and AI model understanding, using modern compiler design principles.

[allenanswerzq]

thanks for collecting my project here, this is the generated design graph for ruff, not sure how good it represents, just having fun..

[allenanswerzq]

commands to reproduce if someone interested: ./target/release/llmcc --dir ../ruff --design-graph --pagerank --top-k 100

[cleder]

https://github.com/NathanDuboisset/importee
Python import checker with Rust backend. Checks import ordering rules in Python projects.
@NathanDuboisset

[NathanDuboisset]

I just created this one as a side project(it works for just one rule). I wanted to touch the rust part myself. I searched a tool like that, found the one by David Seddon, and wanted to use a rust approach to learn. Maybe will not be at the same level as the others. Happy to work on it with people though !