Code Researcher – A Deep Research Agent Tool Launched by Microsoft Research

What is Code Researcher?

Code Researcher is an advanced research Agent tool developed by Microsoft Research, designed specifically to handle large-scale system codebases and their commit histories for the purpose of automatically fixing system crash issues. It operates in three stages: Analysis, Synthesis, and Validation.

In the Analysis stage, Code Researcher uses multi-step reasoning strategies to collect and store contextual information in structured memory, leveraging code semantics, patterns, and commit history. During Synthesis, it generates repair patches based on the gathered context. In the Validation phase, external tools are used to verify the effectiveness of the generated patches.

This tool deeply explores the codebase and commit history to extract global context related to crashes. It supports a variety of reasoning strategies such as control and data flow analysis, pattern matching, and causal analysis based on historical commits. It uses regular expression-based searches to efficiently locate root causes of issues.

Key Features of Code Researcher

• Deep Code Analysis:
  Code Researcher employs multi-step reasoning, integrating code semantics, patterns, and historical commits to thoroughly understand the root causes of crashes.

• Context Collection:
  Information collected during analysis is stored in structured memory to provide rich context for patch generation.

• Code Search:
  Supports regular expression-based searches to locate specific patterns in the codebase efficiently.

• Commit History Analysis:
  Searches historical commit records to identify changes related to current crashes, leveraging past development experience to guide repairs.

• Causal Analysis:
  Analyzes the impact of past commits on current issues to identify the root cause of introduced problems.

• Intelligent Synthesis:
  Generates high-quality repair patches based on collected context, potentially spanning multiple files.

• Filtering and Optimization:
  Filters out irrelevant data during synthesis to focus on crash-relevant context, ensuring accurate and effective patches.

• External Tool Integration:
  Uses external tools to validate whether the generated patches effectively prevent crashes, ensuring correctness and safety.

• Automated Validation Workflow:
  Patches are tested automatically to verify effectiveness, reducing manual intervention and improving repair efficiency.

• Generalization Capability:
  Code Researcher has demonstrated generalizability across large systems such as the Linux kernel and FFmpeg, quickly adapting to different codebases and producing effective patches.

• Repair Suggestions:
  In cases where full automation is not possible, Code Researcher can still provide valuable debugging insights and repair suggestions to accelerate issue resolution.

Technical Principles Behind Code Researcher

• Multi-Step Reasoning and Semantic Analysis:
  It incrementally gathers contextual information using advanced reasoning techniques. Powered by large language models (LLMs), Code Researcher deeply understands code logic and structure, enabling precise localization of crash root causes.

• Commit History Analysis:
  Innovatively analyzes the evolution of bugs through commit history to understand the essence of the problem. This temporal analysis makes it possible to handle massive codebases with millions of lines of code.

• Global Context Collection:
  In the analysis stage, Code Researcher collects global context—such as code snippets, commit records, and symbol definitions—and stores them in structured memory for patch synthesis.

• Deep Exploration and Smart Synthesis:
  Capable of exploring up to 10 related files per analysis trajectory, Code Researcher filters out irrelevant content and uses contextual information to generate effective repair patches.

Project Link for Code Researcher

• arXiv Paper: https://arxiv.org/pdf/2506.11060

Application Scenarios for Code Researcher

• Linux Kernel Crash Repair:
  Code Researcher can automatically identify root causes and generate repair patches for Linux kernel crashes through deep analysis of semantics, patterns, and commit history.

• Enterprise Software Maintenance:
  Automates crash diagnosis and repair for enterprise-level systems, accelerating patch generation through deep inspection of codebases and historical commits.

• Developer Assistance Tool:
  Can act as a powerful assistant for developers, providing in-depth root cause analysis and actionable fix suggestions.

• Automated Testing and Continuous Integration:
  Can be integrated into CI/CD pipelines to automatically detect and fix crash issues, improving software robustness with minimal human intervention.