EvoDev introduces an iterative feature-driven framework with a DAG-based Feature Map for context propagation that improves LLM agent performance on end-to-end software development tasks by 56.8% over the best baseline.
A Model Is Not Built By A Single Prompt: LLM-Based Domain Modeling With Question Decomposition
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Preference-based prompting raises LLM adherence to object-oriented design principles in UML generation but leaves substantial output variance and model-specific differences intact.
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EvoDev: An Iterative Feature-Driven Framework for End-to-End Software Development with LLM-based Agents
EvoDev introduces an iterative feature-driven framework with a DAG-based Feature Map for context propagation that improves LLM agent performance on end-to-end software development tasks by 56.8% over the best baseline.
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Reliability of Large Language Models for Design Synthesis: An Empirical Study of Variance, Prompt Sensitivity, and Method Scaffolding
Preference-based prompting raises LLM adherence to object-oriented design principles in UML generation but leaves substantial output variance and model-specific differences intact.