GenAI提升日常编码效率，复杂任务仍需人工介入
Paradigm shift on Coding Productivity Using GenAI
- 通过提示词迭代与沉浸式环境提升生成代码质量
- 在重构、注释等常规任务中效率提升显著
- 适合需要快速原型开发的工程师团队
生成式AI（GenAI）正推动软件工程向自动化协同编程转型。本研究基于电信与金融科技领域专家的问卷与访谈，分析了Codeium、Amazon Q等工具在工业场景中的应用效果。结果表明，GenAI在常规编码任务（如重构、Javadoc生成）中显著提升生产力，但在复杂、领域相关的开发活动中受限于对代码库上下文的理解不足及对定制化设计规则支持不够。研究提出新编码范式，强调提示词持续优化、沉浸式开发环境构建和自动化代码评估的重要性，以实现高效可靠的GenAI辅助编程。
原文摘要 · Abstract (English)
Generative AI (GenAI) applications are transforming software engineering by enabling automated code co-creation. However, empirical evidence on GenAI's productivity effects in industrial settings remains limited. This paper investigates the adoption of GenAI coding assistants (e.g., Codeium, Amazon Q) within telecommunications and FinTech domains. Through surveys and interviews with industrial domain-experts, we identify primary productivity-influencing factors, including task complexity, coding skills, domain knowledge, and GenAI integration. Our findings indicate that GenAI tools enhance productivity in routine coding tasks (e.g., refactoring and Javadoc generation) but face challenges in complex, domain-specific activities due to limited context-awareness of codebases and insufficient support for customized design rules. We highlight new paradigms for coding transfer, emphasizing iterative prompt refinement, immersive development environment, and automated code evaluation as essential for effective GenAI usage.
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。