A Study of Preconditions and Postconditions as Design Constraints for LLM Code Generation

<p>Large Language Models (LLMs) have significantly advanced automated code generation, but current methods predominantly rely on natural language descriptions. This approach encounters challenges when handling complex, class-level software generation tasks due to inherent ambiguity and under-specification. Few studies have investigated how more formal software engineering constraints, such as explicit preconditions and postconditions, influence class-level generation tasks. This work addresses this gap through a structured evaluation of six state-of-the-art LLMs generating software implementations from systematically designed class-level specifications. Results demonstrate that incorporating explicit design constraints significantly boosts initial generation accuracy (measured via the pass@k metric), particularly in Python but also in Java and C++. Models with fewer parameters or weaker initial performance saw especially pronounced benefits. These findings suggest integrating structured software engineering constraints into LLM-based code generation workflows to enhance accuracy and maintainability in automated software projects.</p>

Name thesis:degree_name

Master of Software Engineering

Level thesis:degree_level

Thesis - Open Access

Discipline thesis:degree_discipline

Electrical Engineering and Computer Science

Year dc:date.available

2025

Author dc:creator

• Newcomb, Luke

• software engineering
• design by contract
• large language models
• code generation

Repository record dc:identifier

https://commons.erau.edu/edt/927

OAI identifier oai:identifier

oai:commons.erau.edu:edt-1917