Prompting Large Language Models to Tackle the Full Software Development Lifecycle: A Case Study
Bowen Li, Wenhan Wu, Ziwei Tang, Lin Shi, John Yang, Jinyang Li, Shunyu Yao, Chen Qian
Abstract
Recent advancements in large language models (LLMs) have significantly enhanced their coding capabilities. However, existing benchmarks predominantly focused on simplified or isolated aspects of coding, such as single-file code generation or repository issue debugging, falling short of measuring the full spectrum of challenges raised by real-world programming activities. In this case study, we explore the performance of LLMs across the entire software development lifecycle with DevEval, encompassing stages including software design, environment setup, implementation, acceptance testing, and unit testing. DevEval features four programming languages, multiple domains, high-quality data collection, and carefully designed and verified metrics for each task. Empirical studies show that current LLMs, including GPT-4, fail to solve the challenges presented within DevEval. Our findings offer actionable insights for the future development of LLMs toward real-world programming applications.
BibTeX
@inproceedings{li-etal-2025-prompting,
title = "Prompting Large Language Models to Tackle the Full Software Development Lifecycle: A Case Study",
author = "Li, Bowen and
Wu, Wenhan and
Tang, Ziwei and
Shi, Lin and
Yang, John and
Li, Jinyang and
Yao, Shunyu and
Qian, Chen and
Hui, Binyuan and
Zhang, Qicheng and
Yu, Zhiyin and
Du, He and
Yang, Ping and
Lin, Dahua and
Peng, Chao and
Chen, Kai",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.502/",
pages = "7511--7531"
}