Code-QA-Bench: Separating Code Reasoning from Documentation Memorization in Repository-Level QA
arXiv:2605.29277v1 Announce Type: cross Abstract: We present Code-QA-Bench, a fully automated framework for synthesizing repository-level code understanding benchmarks that separates genuine code comprehension from documentation recall and pretraining memorization. The framework makes two methodological contributions: (1) an answer-first generation pipeline where a tool-equipped agent explores source code to produce verified gold answers before deriving questions, ensuring every task is grounded in real code structure; and (2) a three-condition experimental design evaluating agents under close
The rapid advancement and adoption of large language models in code generation necessitate more robust and nuanced evaluation benchmarks to accurately assess their capabilities.
This framework provides a critical tool for distinguishing true AI reasoning from memorization, which is essential for developing reliable and genuinely intelligent AI agents capable of complex tasks.
The ability to more accurately benchmark code understanding will accelerate the development of more capable AI agents for software development and related fields.
- · AI agent developers
- · Software engineering firms
- · AI research institutions
- · Code quality assurance platforms
- · AI models that rely heavily on memorization
- · Manual code review processes
- · Traditional code testing methods
Improved evaluation leads to the faster iteration and deployment of AI models for software development.
More reliable AI-powered coding tools could significantly increase developer productivity and reduce software bugs.
The enhanced capability of AI in understanding and generating code could accelerate innovation across numerous technology sectors.
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Read at arXiv cs.AI