yupbank/mini-pysonar — explained in plain English
Analysis updated 2026-07-18 · repo last pushed 2013-06-23
Study how a static analyzer for a dynamic language like Python can be built from scratch.
Use as a reference implementation to build your own linter, IDE autocomplete, or code search tool.
Learn how variable assignments and function call relationships can be tracked without executing code.
| yupbank/mini-pysonar | 0xallam/my-recipe | 0xhassaan/nn-from-scratch | |
|---|---|---|---|
| Stars | — | — | 0 |
| Language | Python | Python | Python |
| Last pushed | 2013-06-23 | 2022-11-22 | — |
| Maintenance | Dormant | Dormant | — |
| Setup difficulty | moderate | moderate | moderate |
| Complexity | 3/5 | 2/5 | 4/5 |
| Audience | researcher | general | developer |
Figures from each repo's GitHub metadata at analysis time.
It's a proof-of-concept, not production-ready, the author has stopped actively developing it.
This is a prototype static analyzer for Python code, a tool that reads Python files and understands what they do without actually running them. Think of it like a spell-checker for code, but instead of looking for typos, it tracks what variables hold, which functions call which other functions, and how data flows through a program. The main benefit is that developers can use this kind of analyzer to find bugs, understand code structure, or provide better autocomplete suggestions in code editors. The analyzer works by parsing Python code and building a map of how it all connects. When it sees a variable assignment, it remembers that. When it sees a function definition or a call to that function, it tracks those relationships too. At a high level, it's like following a thread through your code to understand which pieces depend on which other pieces. The current version handles the basics: variables, assignments, functions, and function calls. It's intentionally kept simple compared to the original version built at Google, which could handle more complex Python features like classes and modules. This project is really a teaching tool and reference implementation. The author created it to show how you might build a static analyzer for a dynamic language like Python, where the type of a variable can change at runtime. Researchers, students, or developers interested in building their own code analysis tools could study how this works and adapt the approach for their own projects. It's not meant to be a production-ready tool you'd use on real codebases, it's more of a proof-of-concept to demonstrate the core ideas. The README is honest about the project's limitations: the author notes they lack motivation to develop it further and put it online hoping it would help others learning the field. If you're building a Python IDE, linter, or code search tool, or if you're curious how static analysis actually works under the hood, this codebase provides a clean, simplified foundation to understand the fundamentals.
A prototype static analyzer that reads Python code without running it, tracking variables and function calls to teach how static analysis works.
Mainly Python. The stack also includes Python.
Dormant — no commits in 2+ years (last push 2013-06-23).
Setup difficulty is rated moderate, with roughly 1h+ to a first successful run.
Mainly researcher.
This repo across BitVibe Labs
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