SuperCoder2.0: Technical Report on Exploring the feasibility of LLMs as Autonomous Programmer

Understand

We present SuperCoder2.0, an advanced autonomous system designed to enhance software development through artificial intelligence.

• The system combines an AI-native development approach with intelligent agents to enable fully autonomous coding.
• Key focus areas include a retry mechanism with error output traceback, comprehensive code rewriting and replacement using Abstract Syntax Tree (ast) parsing to minimize linting issues, code embedding technique for retrieval-augmented generation, and a focus on localizing methods for problem-solving rather than identifying specific line numbers.
• The methodology employs a three-step hierarchical search space reduction approach for code base navigation and bug localization:utilizing Retrieval Augmented Generation (RAG) and a Repository File Level Map to identify candidate files, (2) narrowing down to the most relevant files using a File Level Schematic Map, and (3) extracting 'relevant locations' within these files.