Comparison Between Machine Learning Based Models and Traditional Fault Prediction Approaches for Upgrading Software Reliability

Author(s)	Dinesh Kumar, Dr.Saurabh Charaya
Country	India
Abstract	Maintaining high industry standards is heavily dependent on software stability, which in turn affects product quality, customer happiness, and operational efficiency. Although they have their uses, traditional failure prediction methods aren't always up to snuff when it comes to today's complex and ever-changing software systems. In this study, Conventional research work related to fault prediction and machine learning has been discussed. Organizations can reduce maintenance costs, minimize downtime, and improve overall software quality by proactively addressing reliability issues through the integration of this paradigm into the software development lifecycle. Compared to more conventional methods of fault prediction, the ML-based approach significantly outperforms them in terms of prediction accuracy, flexibility, and scalability, according to empirical assessments. By laying out a solid plan for improving software reliability, this study advances the field and establishes new standards for the business.
Keywords	Software Reliability, Fault Prediction, Machine Learning, Industry Standards, Software Quality
Field	Computer Applications
Published In	Volume 6, Issue 5, September-October 2025
Published On	2025-10-08

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