The paper proposes a neurosymbolic LLM agent that plans via Prolog predicates, but presents no experiments to back its claims of improved precision and coverage on DABStep.
Effort and Size Estimation in Software Projects with Large Language Model-based Intelligent Interfaces
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abstract
The advancement of Large Language Models (LLM) has also resulted in an equivalent proliferation in its applications. Software design, being one, has gained tremendous benefits in using LLMs as an interface component that extends fixed user stories. However, inclusion of LLM-based AI agents in software design often poses unexpected challenges, especially in the estimation of development efforts. Through the example of UI-based user stories, we provide a comparison against traditional methods and propose a new way to enhance specifications of natural language-based questions that allows for the estimation of development effort by taking into account data sources, interfaces and algorithms.
fields
cs.LG 1years
2025 1verdicts
REJECT 1representative citing papers
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Do LLMs Dream of Discrete Algorithms?
The paper proposes a neurosymbolic LLM agent that plans via Prolog predicates, but presents no experiments to back its claims of improved precision and coverage on DABStep.