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, security, and maintainability. CoQuIR provides fine-grained quality annotations for 42,725 queries
The proliferation of AI in software development highlights the critical need for more sophisticated evaluation benchmarks that move beyond mere functional correctness to include code quality, which is now possible due to advancements in AI-driven analysis capabilities.
This benchmark shifts the focus from purely functional code retrieval to quality-aware code retrieval, enabling the development of AI tools that produce more robust, secure, and maintainable software, directly impacting developer productivity and software reliability.
The introduction of CoQuIR means that future code retrieval models and AI-assisted coding tools will likely be optimized not just for finding relevant code, but for finding high-quality relevant code, setting a new standard for development practices.
- · AI-powered code development platforms
- · Software developers
- · Companies prioritizing code quality
- · Code analysis tool providers
- · Companies relying on low-quality code for rapid deployment
- · Developers of code retrieval models without quality metrics
AI models for software development will begin to incorporate quality metrics as a primary objective.
This will lead to an overall improvement in the quality and maintainability of AI-generated and AI-assisted code.
Higher code quality could reduce technical debt, accelerate innovation, and potentially lower the cybersecurity attack surface for software systems.
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Read at arXiv cs.AI