arXiv AI
Adaptive Critical Token-Aware Retrieval for Repository-Level Code Generation
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arXiv Computation and Language
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Enhancing Knowledge Density via Hierarchical Context Pruning for Retrieval-Augmented Repository-Level Code Completion
The paper introduces SaraCoder, a retrieval‑augmented framework that improves knowledge density for repository‑level code completion by pruning redundant context and selectively injecting cross‑file dependency definitions within a fixed token budget. It employs a Hierarchical Feature Optimization module to reduce an over‑complete candidate pool in four stages, freeing tokens for an External‑Aware Identifier Disambiguator. Experiments on CrossCodeEval and RepoEval‑Updated demonstrate that SaraCoder outperforms strong RAG baselines such as GraphCoder and Repocoder across multiple code LLMs without increasing token usage, and achieves synergistic gains when combined with other cross‑file methods.
arXiv Machine Learning
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Code Comprehensor: Hierarchical Documentation and Cross-Modal Retrieval for Large Repositories
Code Comprehensor is a hybrid retrieval‑reasoning framework designed to aid understanding of large codebases. It combines a code dependency callgraph, CodeScribe—which generates hierarchical documentation using code‑aware LLMs—and CodeContrast, a CLIP‑style dual‑encoder that learns aligned text‑code embeddings for fast, semantically precise retrieval. Experiments on OpenCV and a 5M‑LOC proprietary repository show that CodeScribe‑derived supervision improves cross‑modal retrieval accuracy, and LLM‑based evaluation reports over 90% faithfulness.
arXiv AI
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ProjAgent: Procedural Similarity Retrieval for Repository-Level Code Generation
arXiv:2607. 08691v1 Announce Type: cross Abstract: Repository-level code generation requires implementing target functions while accounting for complex cross-file dependencies and project-specific conventions.
arXiv AI
Jul 29
Agent Retrieval Bench: Evaluating Repository Context Retrieval for Coding Agents
arXiv:2607. 24882v1 Announce Type: cross Abstract: Modern coding agents are usually evaluated by whether they eventually produce a correct patch, but patch generation depends on an earlier context-acquisition stage: finding the repository files needed for the task.
arXiv AI
Jul 29
Beyond "What to Retrieve": Uncertainty in Retrieval-Augmented Code Generation
arXiv:2607. 24884v1 Announce Type: cross Abstract: Repository-level code generation relies on heterogeneous evidence whose relevance, compatibility, and completeness are inherently uncertain.
arXiv AI
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DeepDiscovery: A Location-Inference Framework for Task-Level Repository Understanding
arXiv:2606.22906v2 Announce Type: replace-cross Abstract: Large language models have shown strong performance on software engineering (SE) tasks, yet understanding large industrial repositories remai...