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coding-agentresearchopen-sourceprogram-repairast-analysisllmcode-searchautonomous-coding

AutoCodeRover

AutoCodeRover is an autonomous coding agent developed by NUS that excels in program repair by combining LLMs with code search and AST-level analysis. It leverages structured code understanding to achieve strong SWE-bench results, offering a more efficient approach than brute-force code generation.

intermediate2-3 hours4 steps
The play
  1. Understand the AutoCodeRover Architecture
    AutoCodeRover uses a multi-stage approach. First, it identifies the bug location. Then, it retrieves relevant code snippets using code search. Finally, it uses AST-level analysis to generate a patch that fixes the bug while maintaining code structure and semantics.
  2. Explore the Code Search Mechanism
    The agent uses code search to find similar code snippets that might contain relevant solutions or patterns. This helps the LLM generate more accurate and context-aware patches. Investigate how the search queries are constructed and how the results are filtered.
  3. Analyze the AST-Level Patch Generation
    AutoCodeRover uses Abstract Syntax Trees (ASTs) to represent the code structure. This allows the agent to make precise and syntactically correct changes. Examine how the AST is traversed and modified to generate the patch.
  4. Compare with Brute-Force Generation
    Contrast AutoCodeRover's structured approach with brute-force code generation methods. Consider the advantages of AST-level analysis in terms of correctness, efficiency, and maintainability.
Starter code
Start by reading the AutoCodeRover research paper to understand the core concepts and architecture. Then, explore the open-source codebase to see how the different components are implemented.
Source
AutoCodeRover — Action Pack