This research introduces a novel framework for enhancing how Large Language Models approach code reasoning tasks, combining both reasoning and recall capabilities.

Key Insights:

• Introduces code reasoning as a distinct task that tests both logical reasoning and knowledge recall in LLMs
• Develops three comprehensive meta-benchmarks to evaluate code reasoning capabilities
• Proposes the RHDA pipeline (likely Reasoning through Hypothesis Decomposition and Amendment) to improve how LLMs process and solve programming problems
• Demonstrates significant improvements in code reasoning performance compared to standard prompting methods

For engineering teams, this research offers a pathway to develop more reliable code assistants capable of deeper reasoning about programming logic, potentially reducing debugging time and improving code quality.

Unveiling the Magic of Code Reasoning through Hypothesis Decomposition and Amendment