Abstract

Mobile applications are playing a crucial role in today's digital era, across various sectors, including communication, e-commerce, healthcare, and governance. One of the most important problems that software engineers face when deploying these applications is to ensure their quality and reliability. Mobile Application Defect Prediction (MADP) is a new research area that focuses on predicting potential defects in mobile applications before it is released by applying computational intelligence techniques. The paper provides an extensive literature survey of SDP, especially the use of Machine Learning (ML), Deep Learning (DL) and Ensemble Learning techniques for mobile applications. This paper explores how SDP has developed from traditional statistical models to the more sophisticated models based on Artificial Intelligence (AI), and why hybrid deep learning and ensemble models are superior. Important algorithms like Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) and ensemble methods like Bagging, Boosting and Stacking are discussed. Existing research gaps are discussed — notably, the lack of any dedicated mobile application defect prediction model based on hybrid ensembles and deep learning. Finally, the paper presents the motivation and design rationale for a proposed Hybrid MADP-Model which combines CNN-LSTM and ensemble approaches to enhance the predictive accuracy, generalizability and robustness in various mobile application datasets.

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