code-complexity

Codebase intelligence for AI and humans: code health scores, auto-generated docs, git analytics, dead code detection, and architectural decisions via MCP.

Code quality analysis for Python in the age of AI coding.

VS Code extension that grades every function A–D and estimates Big-O time and space, cyclomatic and cognitive complexity for 12 languages. Offline, no API key.

Technical debt and risk analyzer that predicts bug hotspots by combining cognitive complexity, pattern recognition, coverage gaps, information theory, and git history.

Hot/cold codebase analysis: git churn crossed with file complexity, annotated on pull requests

Collection of tools to benchmark, assess code complexity, and plot results, developed for a P3HPC study.

Local-first, multi-language static analysis with inspectable code metrics, offline reports and user-defined policies.

AST-based feature representation for source code, used in the Big-O runtime complexity prediction capstone (Capstone B-10).

Notebooks of all training experiments

Dataset classified by language and runtime complexities

🧩 Análise em tempo real da complexidade assintótica de Tempo (Big O) e Espaço (Big O) para VS Code e editores compatíveis.

Dead code forensics for TypeScript — confidence tiers, evidence chains, and removal that refuses to guess.

Measure your codebase from the terminal — lines per language, duplicate code, complexity, token cost for AI agents, comment drift, change coupling, a refactor verdict per file, and what is safe to hand to an agent. JSON output, CI exit codes, PR diff mode and an MCP server. One dependency, 100% test coverage.

Measures code maintainability as an analog of entropy from statistical physics — for PR gates and whole-repo static analysis.

Codebase analysis for TypeScript, JavaScript, and Python. Dead files, unused exports/imports/deps, cycles, complexity, and health grades — monorepo-aware, zero-config, deterministic Rust CLI. CI baseline ratchet, framework plugins, JSON/SARIF for agents. No tsc. No network.

Replication package for "Not All AI Code Is Equal" — a multi-model comparison of defects, vulnerabilities, and complexity in human- and AI-generated code. Runs Pylint/PMD, Semgrep, and Lizard analyses over 500 Python & Java tasks each from Claude Opus 4.8, Gemini 2.5 Pro, and Qwen2.5-Coder vs. human code.

Go CLI & library to measure code complexity, find technical-debt hotspots (complexity × git churn), detect duplicate code, and map the dependency graph — across Go, Python, JS/TS, Rust, Java, C/C++, C#, Ruby, PHP and any tree-sitter language. Cyclomatic + cognitive complexity, Halstead, Maintainability Index. Ships a Claude Code skill.

A pragmatic tool for measuring whether a codebase is too complex for its task.