Code Performance Optimizer
Code Brisk Hackathon
1st Place
Code Brisk Hackathon
Python + JS
Multi-language support
CI/CD Ready
GitHub Action integration
AST-based
Deep structural analysis
Overview
This project won 1st Place at the Code Brisk Hackathon. We built a tool that automatically analyzes and optimizes code snippets in Python and JavaScript for both performance and readability. The tool uses static analysis techniques to identify common performance anti-patterns, suggest optimizations, and can be integrated directly into CI/CD pipelines for automated code quality enforcement.
Code review is one of the most time-consuming parts of the software development lifecycle, and performance-related issues are among the hardest to catch manually. Developers often write functionally correct code that contains subtle inefficiencies — unnecessary loops, redundant computations, suboptimal data structures, or missed opportunities for built-in optimizations.
Our tool bridges the gap between linters (which focus on style) and profilers (which require runtime execution) by performing deep static analysis that identifies performance anti-patterns at the code structure level.
The Problem
Code review processes focus primarily on correctness and style, while performance optimizations are often deferred or missed entirely. Existing linters catch syntax issues and style violations but rarely identify algorithmic inefficiencies. Profilers require running the code with representative data, which is not always feasible during review. There is a gap in the toolchain for static analysis that identifies performance anti-patterns before code reaches production.
My Role
Developer
I built the static analysis engine and optimization suggestion system, implementing AST (Abstract Syntax Tree) parsing for Python and JavaScript, defining the anti-pattern rule library, and creating the CI/CD integration layer for automated analysis on pull requests.
The Approach
The tool works by parsing source code into an Abstract Syntax Tree (AST), then traversing the tree to match against a library of known anti-patterns. Each pattern has an associated severity level, explanation, and suggested optimization. Results are presented with the original code and the optimized alternative side by side.
We built separate AST parsers for Python and JavaScript, each with language-specific pattern libraries. Python patterns included list comprehension opportunities, unnecessary list materializations, and suboptimal string concatenation. JavaScript patterns covered unnecessary re-renders, closure memory leaks, and synchronous operations that could be parallelized.
The CI/CD integration was designed as a GitHub Action that runs on pull requests, posting optimization suggestions as inline review comments. This minimized friction by putting suggestions exactly where developers would see them during their normal review workflow.
Key Features
What we built
AST-Based Analysis
Deep code analysis using Abstract Syntax Tree parsing to identify structural performance anti-patterns beyond what linters catch.
Multi-Language Support
Separate analysis engines for Python and JavaScript, each with language-specific optimization rules and suggestions.
Side-by-Side Suggestions
Optimization results showing original code alongside the optimized alternative with clear explanations of the performance improvement.
CI/CD Integration
GitHub Action that analyzes pull requests automatically and posts optimization suggestions as inline code review comments.
Tech Stack
Key Lessons
What I took away from this project
AST-based analysis can catch patterns that regex-based linters miss entirely
Developer tools succeed when they integrate into existing workflows rather than requiring new ones
The explanation of why an optimization matters is as valuable as the optimization itself
Building for two languages simultaneously forces better abstraction in the analysis engine
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