CoQuIR: A Comprehensive Benchmark for Code Quality-Aware Information Retrieval

📰 ArXiv cs.AI

Learn how CoQuIR benchmark evaluates code quality-aware information retrieval across correctness, efficiency, readability, and maintainability dimensions

advanced Published 8 Jun 2026
Action Steps
  1. Build a code retrieval system using CoQuIR benchmark to evaluate its performance
  2. Run experiments to compare the effectiveness of different code retrieval models
  3. Configure CoQuIR to assess code quality across multiple dimensions
  4. Test the robustness of CoQuIR on multilingual codebases
  5. Apply CoQuIR to real-world software development projects to improve code reuse and debugging
Who Needs to Know This

Software engineers, researchers, and developers can benefit from understanding CoQuIR to improve code retrieval and reuse in their projects

Key Insight

💡 CoQuIR is the first large-scale benchmark to evaluate code retrieval systems across multiple dimensions of software quality

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🚀 Introducing CoQuIR: a comprehensive benchmark for code quality-aware information retrieval #CoQuIR #CodeRetrieval #SoftwareQuality

Key Takeaways

Learn how CoQuIR benchmark evaluates code quality-aware information retrieval across correctness, efficiency, readability, and maintainability dimensions

Full Article

Title: CoQuIR: A Comprehensive Benchmark for Code Quality-Aware Information Retrieval

Abstract:
arXiv:2506.11066v3 Announce Type: replace-cross Abstract: Code retrieval is essential in modern software development, as it boosts code reuse and accelerates debugging. However, current benchmarks primarily emphasize functional relevance while neglecting critical dimensions of software quality. Motivated by this gap, we introduce CoQuIR, the first large-scale, multilingual benchmark specifically designed to evaluate quality-aware code retrieval across four key dimensions: correctness, efficiency
Read full paper → ← Back to Reads

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