Role of AI in Application Engineering: Intelligent Error Detection
DOI:
https://doi.org/10.52783/kjsdd.282Keywords:
Artificial Intelligence, Test Driven Design (TDD), Debugging, Railtown AI, Decision trees, Neural networks, Support vector machines and Random forests, K-fold cross validation]Abstract
The use of artificial intelligence (AI) in application engineering specifically, intelligent error detection is highlighted in this work. Some important components of artificial intelligence (AI) in application engineering-intelligent mistake detection are pattern recognition, real-time data monitoring, and predictive analysis. Several tactics have been effective. Some AI models look at version control histories to identify code changes that might introduce issues. Others use natural language processing to look for differences between code and documentation. Runtime monitoring solutions utilize machine learning to detect anomalous activity that suggests underlying issues. The integration of these technologies is resulting in more reliable apps with shorter debugging cycles for development teams. AI helps developers instead of replacing human expertise by handling repetitive detection tasks, allowing them to focus on technical improvements and innovative problem-solving. Simply said, AI can be used for intelligent error detection since it contributes to early bug detection, developer efficiency, and software quality maintenance.
