Inspiration
The idea for this project came from trying to demo a project written at a previous hackathon and realizing just a year or two later it’s already out of date and unusable.
Without continuous maintenance, software quickly falls behind. Expensive engineering resources are devoted to maintaining and updating legacy applications instead of developing new revenue-generating features.
What it does
Custodian uses a combination of machine learning and semantic analysis to keep code maintainable, secure, and efficient.
How we built it
The deep learning models are based on GPT-3 and the semantic analysis uses heuristic analysis of abstract syntax trees (ASTs).
The tech stack uses a combination of a GitHub bot written in Node.js / Express / JavaScript / TypeScript and a web interface written in JavaScript / React / Chakra UI.
Challenges we ran into
Few-shot learning with GPT3 on large inputs GPT3 sometimes gets stuck in infinite whitespace
Accomplishments that we're proud of
Getting a semantic AST approach for refactoring to combine well with the GPT-3 deep learning model
What we learned
- GitHub authentication/API
- AST parsing
- GPT3 Prompt Engineering
What's next for Custodian
Continue to iterate on the model and work with interested customers.
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