Abstract
Cross-project defect prediction (CPDP) is a hot study that predicts defects in the new project by utilizing the model trained on the data from other projects. However, existing CPDP methods usually assume that source and target projects have the same metrics. Heterogeneous defect prediction (HDP) is proposed and has attracted increasing attention, which refers to the metric sets from source and target projects are different in CPDP. HDP conducts prediction model using the instances with heterogeneous metrics from external projects and then use this model to predict defect-prone software instances in source project. However, building HDP methods is challenging including the distribution difference between source and target projects with heterogeneous metrics. In this paper, we propose a Deep adversarial learning based HDP (DHDP) approach. DHDP leverages deep neural network to learn nonlinear transformation for each project to obtain common feature represent, which the heterogeneous data from different projects can be compared directly. DHDP consists of two parts: a discriminator and a classifier that compete with each other. A classifier tries to minimize the similarity across classes and maximize the inter-class similarity. A discriminator tries to distinguish the source of instances that is source or target project on the common feature space. Expensive experiments are performed on 10 public projects from two datasets in terms of F-measure and G-measure. The experimental results show that DHDP gains superior prediction performance improvement compared to a range of competing methods.
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Funding
This paper was supported by the National Natural Science Foundation of China (61802208 and 61772286), China Postdoctoral Science Foundation Grant 2019M651923, and Natural Science Foundation of Jiangsu Province of China (BK0191381).
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Sun, Y., Sun, Y., Wu, F., Jing, XY. (2021). Deep Adversarial Learning Based Heterogeneous Defect Prediction. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2021. Lecture Notes in Computer Science(), vol 12736. Springer, Cham. https://doi.org/10.1007/978-3-030-78609-0_28
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