Software aging remains a persistent challenge in long-running software systems because performance degrades over time through resource exhaustion, memory leaks and related faults. To examine how supervised learning techniques are used to address this problem, this paper presents a search-based literature survey of 93 studies selected through predefined search strings and inclusion and exclusion criteria. The survey focuses on Linear Regression (LR), Support Vector Machines (SVM), K-Nearest Neighbours (KNN) and Backpropagation (BP) neural networks, both as standalone models and in hybrid configurations for software aging, software defect prediction and related reliability tasks. It analyses the modelling roles of these techniques, the reported evaluation settings and the target system types, and reviews BP-based hybrids that integrate LR, SVM, KNN, Random Forest, Decision Tree and Naive Bayes with feedforward, recurrent and convolutional neural networks. The findings identify recurring hybrid patterns, summarise the strengths and limitations of the model families and highlight open issues, including limited comparative evaluation and weak links between prediction models and concrete maintenance or rejuvenation decisions.
Alshammari et al. (Wed,) studied this question.