Abstract
Developing successful software with no defects is one of the main goals of software projects. In order to provide a software project with the anticipated software quality, the prediction of software defects plays a vital role. Machine learning, and particularly deep learning, have been advocated for predicting software defects, however both suffer from inadequate accuracy, overfitting, and complicated structure. In this paper, we aim to address such issues in predicting software defects. We propose a novel structure of 1 Dimensional Convolutional Neural Network (1D-CNN), a deep learning architecture to extract useful knowledge, identifying and modelling the knowledge in the data sequence, reduce overfitting, and finally, predict whether the units of code are defects prone. We design large-scale empirical studies to reveal the proposed model's effectiveness by comparing four established traditional machine learning baseline models and four state-of-the-art baselines in software defect prediction based on the NASA datasets. The experimental results demonstrate that in terms of f-measure, an optimal and modest 1D CNN with a dropout layer outperforms baseline and state-of-the-art models by 66.79% and 23.88%, respectively, in ways that minimize overfitting and improving prediction performance for software defects. According to the results, 1D-CNN seems to be successful in predicting software defects and may be applied and adopted for a practical problem in software engineering. This, in turn, could lead to saving software development resources and producing more reliable software.