SynH-Rank: Quality-Aware Code Search via Diverse Data Synthesis and Hierarchical Ranking Training
Researchers introduce SynH-Rank, a code reranking framework that leverages large language model-driven data synthesis and hierarchical ranking training to prioritize code quality attributes such as execution speed and memory usage, in addition to relevance. They present a new benchmark dataset with 4,209 code pairs and two novel evaluation metrics, showing that SynH-Rank improves quality-aware accuracy by 20.15% over backbone models and outperforms standard relevance-only approaches.
Why it matters: This work advances code search by enabling systems to consider non-functional qualities like efficiency and maintainability, which are important for practical software development.
Full story at: arXiv Software Engineering ↗