A Cross Validation of OSS Reliability Assessment Based on Deep Multimodal and Multitask Learning - Jiang, Shan; Tamura, Yoshinobu; Yamada, Shigeru (Journal of Graphic Era University, 2025)
In recent years, open source software (OSS) has become ubiquitous in every field of our daily life. Hence, the OSS’s reliability is a significant challenge. The traditional models, like the software reliability growth model, can not handle a large scale of data efficiently, while the deep learning provides an effective method. This paper proposes a multi-input multi-output deep neural network to predict the fault detection time intervals and the fault modification time intervals simultaneously. Additionally, we present several numerical examples with a cross validation conducted with 3 types of data splits.