REVIEW 1 cited by
LLM4TDD: Best Practices for Test Driven Development Using Large Language Models
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original 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 Models to help generate code. 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.
Forward citations
Cited by 1 Pith paper
-
REACCEPT: Automated Co-evolution of Production and Test Code Based on Dynamic Validation and Large Language Models
REACCEPT uses GPT-4 with retrieval and compile/test/coverage feedback to identify and rewrite outdated Java test code, reporting higher success than prior methods.
Discussion (0). Continue with ORCID to comment.