  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.
