Prompting Large Language Models to Tackle the Full Software Development Lifecycle: A Case Study
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916522252828672 |
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| author | Li, Bowen Wu, Wenhan Tang, Ziwei Shi, Lin Yang, John Li, Jinyang Yao, Shunyu Qian, Chen Hui, Binyuan Zhang, Qicheng Yu, Zhiyin Du, He Yang, Ping Lin, Dahua Peng, Chao Chen, Kai |
| author_facet | Li, Bowen Wu, Wenhan Tang, Ziwei Shi, Lin Yang, John Li, Jinyang Yao, Shunyu Qian, Chen Hui, Binyuan Zhang, Qicheng Yu, Zhiyin Du, He Yang, Ping Lin, Dahua Peng, Chao Chen, Kai |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_08604 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Prompting Large Language Models to Tackle the Full Software Development Lifecycle: A Case Study Li, Bowen Wu, Wenhan Tang, Ziwei Shi, Lin Yang, John Li, Jinyang Yao, Shunyu Qian, Chen Hui, Binyuan Zhang, Qicheng Yu, Zhiyin Du, He Yang, Ping Lin, Dahua Peng, Chao Chen, Kai Computation and Language Software Engineering 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. |
| title | Prompting Large Language Models to Tackle the Full Software Development Lifecycle: A Case Study |
| topic | Computation and Language Software Engineering |
| url | https://arxiv.org/abs/2403.08604 |