Simplified deep forest model based just-in-time defect prediction for android mobile apps

Zhao, K., Xu, Z., Zhang, T., Tang, Y. and Yan, M. (2021) Simplified deep forest model based just-in-time defect prediction for android mobile apps. IEEE Transactions on Reliability, 70(2), pp. 848-859. (doi: 10.1109/TR.2021.3060937)

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The popularity of mobile devices has led to an explosive growth in the number of mobile apps in which Android mobile apps are the mainstream. Android mobile apps usually undergo frequent update due to new requirements proposed by users. Just-in-time (JIT) defect prediction is appropriate for this scenario for quality assurance because it can provide timely feedback by determining whether a new code commit will introduce defects into the apps. As defect-prediction performance usually relies on the quality of the data representation and the used classification model, in this work, we propose a model, called Simplified Deep Forest (SDF), to conduct JIT defect prediction for Android mobile apps. SDF modifies a state-of-the-art deep forest model by removing the multigrained scanning operation that is designed for data with a high-dimensional feature space. It uses a cascade structure with ensemble forests for representation learning and classification. We conduct experiments on 10 Android mobile apps and experimental results show that SDF performs significantly better than comparative methods in terms of 3 performance indicators.

Item Type:Articles
Additional Information:This work was supported in part by the National Natural Science Foundation of China under Grant 62002034, in part by the China Postdoctoral Science Foundation under Grant 2020M673137, in part by the Fundamental Research Funds for the Central Universities under Grant 2020CDCGRJ072 and Grant 2020CDJQY-A021, in part by the Natural Science Foundation of Chongqing in China under Grant cstc2020jcyj-bshX0114, in part by the Science and Technology Development Fund of Macau under Grant 0047/2020/A1, and in part by the Faculty Research Grant Projects of MUST under Grant FRG-20-008-FI.
Glasgow Author(s) Enlighten ID:Tang, Dr Yutian
Authors: Zhao, K., Xu, Z., Zhang, T., Tang, Y., and Yan, M.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:IEEE Transactions on Reliability
ISSN (Online):1558-1721
Published Online:17 March 2021

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