Developer tooling
Data structures and algorithms
A typed Python library of classic data structures and algorithms, each documented with its complexity and checked against the standard library.
01 Problem
Hand-written data structures usually look right and pass a few chosen examples, so subtle bugs survive: a hash map lookup that stops early after a delete, a balanced tree missing a rotation, a sort that silently loses stability.
02 Approach
- Small, readable modules typed with Python 3.12 generics that implement the standard collection interfaces, so each behaves like a normal dict, set or list.
- Property-based tests replay random sequences of operations against a trusted reference such as dict, heapq or sorted.
- Benchmarks against the built-ins are published with the machine and date they were measured on.
03 Evidence
- 300 tests with 100% line and branch coverage
- mypy strict and ruff in CI on Python 3.12, 3.13 and Windows
- Deliberately planted bugs each caught with a minimal failing example
The repository README explains how to run each test suite, and its continuous integration history is public.