Software Reliability Intensification: Artificial Intelligence Outlook
Keywords:
Artificial Intelligence (AI), Deep learning, Failure prediction, Fault detection, Fault tolerance, Long Short-Term Memory (LSTM), Machine Learning (ML), Neural network, Predictive maintenance, Real-time adaptation, Scalability, Software reliability, Software Reliability Growth Models (SRGM)Abstract
Software reliability is a crucial factor influencing overall software quality and poses considerable challenges in contemporary complex systems. Conventional reliability models often overlook the fluid characteristics of software environments, which underscore the need to incorporate Artificial Intelligence (AI) and Machine Learning (ML) methodologies. This research explores the application of AI-enhanced techniques to improve predictive accuracy, minimize failure occurrences, and streamline software maintenance processes. Notably, deep learning architectures, particularly Long Short-Term Memory (LSTM) networks, exhibit exceptional efficacy in forecasting software failures. The findings of this study emphasize the importance of sophisticated AI algorithms in reconciling the discrepancies between theoretical frameworks and real-world reliability evaluations.
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