Understanding Specification-Driven Code Generation with LLMs: An Empirical Study Design
Abstract
Large Language Models (LLMs) are increasingly integrated into software development workflows, yet their behavior in structured, specification-driven processes remains poorly understood. This paper presents an empirical study design using CURRANTE, a Visual Studio Code extension that enables a human-in-the-loop workflow for LLM-assisted code generation. The tool guides developers through three sequential stages--Specification, Tests, and Function--allowing them to define requirements, generate and refine test suites, and produce functions that satisfy those tests. Participants will solve medium-difficulty problems from the LiveCodeBench dataset, while the tool records fine-grained interaction logs, effectiveness metrics (e.g., pass rate, all-pass completion), efficiency indicators (e.g., time-to-pass), and iteration behaviors. The study aims to analyze how human intervention in specification and test refinement influences the quality and dynamics of LLM-generated code. The results will provide empirical insights into the design of next-generation development environments that align human reasoning with model-driven code generation.
Cite
@article{arxiv.2601.03878,
title = {Understanding Specification-Driven Code Generation with LLMs: An Empirical Study Design},
author = {Giovanni Rosa and David Moreno-Lumbreras and Gregorio Robles and Jesús M. González-Barahona},
journal= {arXiv preprint arXiv:2601.03878},
year = {2026}
}
Comments
This paper is a Stage 1 Registered Report. The study protocol and analysis plan were peer reviewed and accepted at SANER 2026 with a Continuity Acceptance (CA) score for Stage 2