11位专家评估大模型对软件开发影响，发现提效显著但需警惕依赖风险。
From Idea to Implementation: Evaluating the Influence of Large Language Models in Software Development -- An Opinion Paper
- 访谈11位专家，分析大模型在编码、调试中的实际应用
- 普遍认为可提升开发效率，缩短编码时间
- 关注过度依赖与伦理问题，呼吁负责任使用
Transformer架构的出现是自然语言处理的重要转折点。基于该架构的BERT和GPT等大语言模型（LLMs）已在软件开发、教育等多个领域广泛应用。随着ChatGPT、Bard等模型向公众开放，其在代码生成、调试和文档撰写等方面的潜力被充分展现，推动了其在软件开发中的深度集成。本研究通过收集并分析11位专家关于大模型在软件开发中应用的经验与观点，提炼出可供参考的实践洞见。总体来看，专家评价积极，普遍认为大模型能有效提升开发效率、减少编码时间。同时，也指出潜在风险，如对模型的过度依赖、代码质量不可控及伦理问题。研究强调应建立负责任的集成策略，以实现技术优势与风险管控的平衡。
原文摘要 · Abstract (English)
The introduction of transformer architecture was a turning point in Natural Language Processing (NLP). Models based on the transformer architecture such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-Trained Transformer (GPT) have gained widespread popularity in various applications such as software development and education. The availability of Large Language Models (LLMs) such as ChatGPT and Bard to the general public has showcased the tremendous potential of these models and encouraged their integration into various domains such as software development for tasks such as code generation, debugging, and documentation generation. In this study, opinions from 11 experts regarding their experience with LLMs for software development have been gathered and analysed to draw insights that can guide successful and responsible integration. The overall opinion of the experts is positive, with the experts identifying advantages such as increase in productivity and reduced coding time. Potential concerns and challenges such as risk of over-dependence and ethical considerations have also been highlighted.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。