The escalating complexity of software systems and accelerating development cycles pose a significant challenge in managing code errors and implementing business logic. Traditional techniques, while cornerstone for software quality assurance, exhibit limitations in handling intricate business logic and extensive codebases. To address these challenges, we introduce the Intelligent Code Analysis Agent (ICAA), a novel concept combining AI models, engineering process designs, and traditional non-AI components. The ICAA employs the capabilities of large language models (LLMs) such as GPT-3 or GPT-4 to automatically detect and diagnose code errors and business logic inconsistencies. In our exploration of this concept, we observed a substantial improvement in bug detection accuracy, reducing the false-positive rate to 66\% from the baseline's 85\%, and a promising recall rate of 60.8\%. However, the token consumption cost associated with LLMs, particularly the average cost for analyzing each line of code, remains a significant consideration for widespread adoption. Despite this challenge, our findings suggest that the ICAA holds considerable potential to revolutionize software quality assurance, significantly enhancing the efficiency and accuracy of bug detection in the software development process. We hope this pioneering work will inspire further research and innovation in this field, focusing on refining the ICAA concept and exploring ways to mitigate the associated costs.

翻译：随着软件系统复杂性不断攀升、开发周期持续加速，代码错误管理与业务逻辑实现的挑战日益严峻。传统技术虽为软件质量保障的基石，但在处理复杂业务逻辑与大规模代码库时显露出局限。为应对这些挑战，我们提出智能代码分析代理（ICAA）这一新概念，该概念融合了AI模型、工程流程设计及传统非AI组件。ICAA利用GPT-3或GPT-4等大型语言模型（LLM）的能力，可自动检测与诊断代码错误及业务逻辑不一致。在探索此概念的过程中，我们观察到缺陷检测准确率显著提升：误报率从基准的85%降至66%，召回率达到60.8%的较高水平。然而，与LLM相关的令牌消耗成本（尤其是每行代码的平均分析成本）仍是广泛采用的重要考量。尽管存在这一挑战，我们的研究结果表明，ICAA具有颠覆软件质量保障的巨大潜力，能显著提升软件开发过程中缺陷检测的效率与准确率。我们期待这项开创性工作能激发该领域的进一步研究创新，重点聚焦于完善ICAA概念并探索降低相关成本的路径。