Enhancing Software Reliability: An AI-based Predictive Maintenance Model for Proactive Error Detection and System Optimization

The fast-paced digital ecosystem of today calls for software systems to be reliable in ensuring the efficiency of operations and continuity of business. Reactive maintenance, the traditional approach, often leads to costly downtime and inefficient use of resources. This research suggests an AI-based predictive maintenance model, using Adaptive Temporal Attention Mechanism-LSTM (ATAM-LSTM), to proactively detect errors and optimize system performance. This model will be based on integrating the time-series data of CPU usage, memory utilization, and disk I/O statistics besides error logs and system alerts to predict system failure and anomalies in performance. In the preprocessing step, the data will be cleaned, normalized, and arranged for optimum input to the model. Data collection, data preprocessing, model development, and evaluation will contribute to the possible detection of potential failure conditions and suggest optimization strategies to improve system performance, thus increasing the reliability of software developed by this approach. Experimental results reveal that the proposed method can significantly reduce downtime, improve error detection accuracy, and optimize resource utilization in software systems.