Leveraging Test Driven Development with Large Language Models for Reliable and Verifiable Spreadsheet Code Generation: A Research Framework

Large Language Models (LLMs), such as ChatGPT, are increasingly leveraged for generating both
traditional software code and spreadsheet logic. Despite their impressive generative capabilities,
these models frequently exhibit critical issues such as hallucinations, subtle logical inconsistencies,
and syntactic errors, risks particularly acute in high stakes domains like financial modelling and
scientific computations, where accuracy and reliability are paramount. This position paper proposes a
structured research framework that integrates the proven software engineering practice of Test-Driven
Development (TDD) with Large Language Model (LLM) driven generation to enhance the correctness
of, reliability of, and user confidence in generated outputs. We hypothesise that a "test first"
methodology provides both technical constraints and cognitive scaffolding, guiding LLM outputs
towards more accurate, verifiable, and comprehensible solutions. Our framework, applicable across
diverse programming contexts, from spreadsheet formula generation to scripting languages such as
Python and strongly typed languages like Rust, includes an explicitly outlined experimental design
with clearly defined participant groups, evaluation metrics, and illustrative TDD based prompting
examples. By emphasising test driven thinking, we aim to improve computational thinking, prompt
engineering skills, and user engagement, particularly benefiting spreadsheet users who often lack
formal programming training yet face serious consequences from logical errors. We invite
collaboration to refine and empirically evaluate this approach, ultimately aiming to establish
responsible and reliable LLM integration in both educational and professional development practices.