Document Type
Article
Source Publication Title
2024 International Workshop on Large Language Models for Code
DOI
https://dl.acm.org/doi/10.1145/3643795.3648382
Abstract
In today’s society, we are becoming increasingly dependent on software systems. However, we also constantly witness the negative impacts of buggy software. Program synthesis aims to improve software correctness by automatically generating the program given an outline of the expected behavior. For decades, program synthesis has been an active research field, with recent approaches looking to incorporate Large Language Model. This paper explores the concept of LLM4TDD, where we guide Large Language Models to generate code iteratively using a test-driven development methodology. We conduct an empirical evaluation using ChatGPT and coding problems from LeetCode to investigate the impact of different test, prompt and problem attributes on the efficacy of LLM4TDD.
Publication Date
9-10-2024
Language
English
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Piya, Sanyogita and Sullivan, Allison, "LLM4TDD: Best Practices for Test Driven Development Using Large Language Models" (2024). Association of Computing Machinery Open Access Agreement Publications-Archive. 96.
https://mavmatrix.uta.edu/utalibraries_acmoapubs/96