SkelDPO improves code generation efficiency by 2-7% over prior DPO methods via joint preference losses on full code and efficiency-critical skeletons.
Llm4effi: Leveraging large language models to enhance code effi- ciency and correctness
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EffiSkel improves LLM-generated code efficiency by supervising on extracted structural efficiency skeletons via multi-task learning of code generation and skeleton prediction.
MOSAIC structures LLM-based model selection via memory-grounded blueprints and failure-aware RL, reporting gains in performance and traceability on financial time-series tasks over AutoML and agent baselines.
Survey mapping LLM applications in software quality assurance to established standards including ISO/IEC 12207, ISO 25010, CMMI, and TMM, with case studies, challenges, and future directions.
citing papers explorer
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SkelDPO: A Skeleton-Guided Direct Preference Optimization Framework for Efficient Code Generation
SkelDPO improves code generation efficiency by 2-7% over prior DPO methods via joint preference losses on full code and efficiency-critical skeletons.
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Chiseling Out Efficiency: Structured Skeleton Supervision for Efficient Code Generation
EffiSkel improves LLM-generated code efficiency by supervising on extracted structural efficiency skeletons via multi-task learning of code generation and skeleton prediction.
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MOSAIC: Modular Orchestration for Structured Agentic Intelligence and Composition
MOSAIC structures LLM-based model selection via memory-grounded blueprints and failure-aware RL, reporting gains in performance and traceability on financial time-series tasks over AutoML and agent baselines.