Fine-Tuning Models for Automated Code Review Feedback
2026-05-14T08:51:52Z•29c1f734d67e1549e75a4dd51e815df0ccc3eca7703f2de55d2b0b843dd54e65
AgentLensCode LlamaLLMPEFTSWE agentsSWE-CycleUIBenchKitVFC detectionagentic interpretationassured translationautomated code reviewbenchmarkscomparative binary analysiscozylarge language modelsprogram analysisprotocol-driven developmentsoftware assurancesoftware governancesoftware supply chainusability requirementsvulnerability-fixing commits
What happened
Collection of recent software-engineering and ML-for-code papers: key findings include that parameter-efficient fine-tuning (PEFT) of Code Llama substantially improves automated code-review feedback quality compared to prompt engineering; LLMs can extract usability requirements from user reviews but are prompt-sensitive; a formal "agentic interpretation" framework is proposed to make LLM-driven program analysis more evidence-driven; "cozy" enables comparative binary analysis to assure semantic equivalence between unsafe C code and translations to memory-safe languages; Protocol-Driven-Developm
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_se
- Record identifier
- 29c1f734d67e1549e75a4dd51e815df0ccc3eca7703f2de55d2b0b843dd54e65
- Enrichment time
- 2026-05-14T08:51:52Z
- AI-assisted enrichment
- Yes
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