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Prompting Large Language Models to Tackle the Full Software Development Lifecycle: A Case Study

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arxiv 2403.08604 v3 pith:AQJGAKPN submitted 2024-03-13 cs.CL cs.SE

Bowen Li , Wenhan Wu , Ziwei Tang , Lin Shi , John Yang , Jinyang Li , Shunyu Yao , Chen Qian
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Binyuan Hui Qicheng Zhang Zhiyin Yu He Du Ping Yang Dahua Lin Chao Peng Kai Chen
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classification cs.CLcs.SE
keywords llmsdevelopmentdevevalprogrammingsoftwarecasechallengescoding
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Forward citations

Cited by 5 Pith papers

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