AN INTELLIGENT NHPP-BASED SOFTWARE RELIABILITY GROWTH MODEL ENHANCED WITH DEEP LEARNING FOR CROSS-PLATFORM FAILURE PREDICTION

Modern software ecosystems especially those driven by open-source collaboration and continuous integration/continuous deployment (CI/CD) practices exhibit rapidly evolving and non-stationary behavior. Such dynamics pose significant challenges to conventional Software Reliability Growth Models (SRGMs). Traditional models based on the Non-Homogeneous Poisson Process (NHPP) typically assume fixed or smoothly varying failure detection rates, limiting their effectiveness in capturing real-time failure trends within highly dynamic and frequently updated code repositories. To overcome these limitations, this study proposes a hybrid predictive framework that integrates NHPP-based reliability modeling with Long Short-Term Memory (LSTM) networks for adaptive and real-time failure forecasting. The proposed Deep Learning Augmented NHPP (DL-NHPP) model enhances the classical failure intensity function by incorporating temporal patterns learned from repository-level signals, including commit frequency, issue reports, and developer activity metrics. Through this integration, the model dynamically adjusts failure rate estimations in response to evolving development behaviors. The framework is evaluated on five large-scale open-source repositories, demonstrating substantial predictive improvements. Experimental results show a reduction of more than 30% in Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) compared to conventional NHPP-based SRGMs. These findings indicate that the DL-NHPP approach provides a scalable, interpretable, and robust solution for real-time software reliability prediction in modern, continuously evolving development environments.