Enhancing Software Reliability through Anomaly Detection: Implementing Variational Autoencoders for Real-time Performance Monitoring and Error Prediction

To secure performance with minimal system downtimes, the reliability of any software is the primary focus. This paper proposes a combined strategy of using deep learning techniques in an anomaly detection setting for software systems, namely the variational autoencoder or VAE. It discusses an approach that begins with a collection of data about, and preprocessing of, historical software performance like critical metrics, for instance, CPU usage, response time, and system logs. Preprocessing involves the implementation of Z-score normalization for feature scale normalization and interpolation-based imputation for time-series data within the case of the missing values. Once the data is set, an autoencoder is engaged to learn the normal system behavior, with any deviations detected through reconstruction error. This is followed by a reiteration phase involving the continuous performance enhancement of the model and the threshold adjustment to seek the highest level of accuracy in anomaly detection. The working of the proposed system is experimentally verified under real conditions, with continuous analysis of program operation and online responses for any anomaly. Such an efficient but scalable method increases the quality of software; moreover, with the ability to alert when an anomaly occurs prior to any catastrophic failures, system reliability and effectiveness will thus be improved.