ProjAgent introduces procedural similarity—retrieving code with matching computational logic—via LLM hidden-state projections, improving repository-level code generation to 41.14% Pass@1 on REPOCOD.
What to Retrieve for Effective Retrieval-Augmented Code Generation? An Empirical Study and Beyond
6 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Repository-level code generation remains challenging due to complex code dependencies and the limitations of large language models (LLMs) in processing long contexts. While retrieval-augmented generation (RAG) frameworks are widely adopted, the effectiveness of different retrieved information sources-contextual code, APIs, and similar snippets-has not been rigorously analyzed. Through an empirical study on two benchmarks, we demonstrate that in-context code and potential API information significantly enhance LLM performance, whereas retrieved similar code often introduces noise, degrading results by up to 15%. Based on the preliminary results, we propose AllianceCoder, a novel context-integrated method that employs chain-of-thought prompting to decompose user queries into implementation steps and retrieves APIs via semantic description matching. Through extensive experiments on CoderEval and RepoExec, AllianceCoder achieves state-of-the-art performance, improving Pass@1 by up to 20% over existing approaches.
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TICoder improves repository-level code generation by 11.52% over prior methods through test-driven planning and implementation-aware code reuse on standard benchmarks.
VeriCache turns lossy KV cache compression into lossless LLM inference by drafting with compressed cache and verifying drafts with full cache, achieving up to 4x throughput with identical outputs.
A review of 114 studies creates taxonomies for code and data quality issues, formalizes 18 propagation mechanisms from training data defects to LLM-generated code defects, and synthesizes detection and mitigation techniques.
Retriever-side choices, particularly the retrieval algorithm, exert more influence on RAG performance than generator selection across code generation, summarization, and repair tasks.
Replication finds Java security API misuse persists in current LLMs but is reduced by external knowledge in a model-dependent manner.
citing papers explorer
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ProjAgent: Procedural Similarity Retrieval for Repository-Level Code Generation
ProjAgent introduces procedural similarity—retrieving code with matching computational logic—via LLM hidden-state projections, improving repository-level code generation to 41.14% Pass@1 on REPOCOD.
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TICoder: A Repository-Level Code Generation Framework with Test-Driven Planning and Implementation-Aware Reuse
TICoder improves repository-level code generation by 11.52% over prior methods through test-driven planning and implementation-aware code reuse on standard benchmarks.
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VeriCache: Turning Lossy KV Cache into Lossless LLM Inference
VeriCache turns lossy KV cache compression into lossless LLM inference by drafting with compressed cache and verifying drafts with full cache, achieving up to 4x throughput with identical outputs.
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Bridging Generation and Training: A Systematic Review of Quality Issues in LLMs for Code
A review of 114 studies creates taxonomies for code and data quality issues, formalizes 18 propagation mechanisms from training data defects to LLM-generated code defects, and synthesizes detection and mitigation techniques.
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Not All RAGs Are Created Equal: A Component-Wise Empirical Study for Software Engineering Tasks
Retriever-side choices, particularly the retrieval algorithm, exert more influence on RAG performance than generator selection across code generation, summarization, and repair tasks.
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R+R: Reassessing Java Security API Misuse in Current LLMs: A Replication on JCA and JSSE APIs with External Security Knowledge
Replication finds Java security API misuse persists in current LLMs but is reduced by external knowledge in a model-dependent manner.