Role of AI in Application Engineering: Intelligent Error Detection

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.

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Publication Details

Journal
Kuwait Journal of Software Design and Development
Published
2024-12-26
DOI
https://doi.org/10.52783/kjsdd.282
Primary Topic
Fault Detection and Control Systems
Type
article
Field-Weighted Citation Impact
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article

Role of AI in Application Engineering: Intelligent Error Detection

Madhavi Kadiyala
Kuwait Journal of Software Design and Development
Fault Detection and Control Systems
article

Role of AI in Application Engineering: Intelligent Error Detection

Madhavi Kadiyala
article en

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.

Kuwait Journal of Software Design and DevelopmentVol. 2(1)
Film Independent (US)
Openalex Percentile: Top 65%
Fault Detection and Control Systems
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