Software defect prediction is one of the most popular research topics in software engineering. It aims to predict defect‐prone software modules before defects are discovered, therefore it can be used to better prioritise software quality assurance effort. In recent years, especially for recent 3 years, many new defect prediction studies have been proposed. The goal of this study is to comprehensively review, analyse and discuss the state‐of‐the‐art of defect prediction. The authors survey almost 70 representative defect prediction papers in recent years (January 2014–April 2017), most of which are published in the prominent software engineering journals and top conferences. The selected defect prediction papers are summarised to four aspects: machine learning‐based prediction algorithms, manipulating the data, effort‐aware prediction and empirical studies. The research community is still facing a number of challenges for building methods and many research opportunities exist. The identified challenges can give some practical guidelines for both software engineering researchers and practitioners in future software defect prediction.

Learning an expressive representation from multi-view data is a key step in various real-world applications. In this paper, we propose a Semi-supervised Multi-view Deep Discriminant Representation Learning (SMDDRL) approach. Unlike existing joint or alignment multi-view representation learning methods that cannot simultaneously utilize the consensus and complementary properties of multi-view data to learn inter-view shared and intra-view specific representations, SMDDRL comprehensively exploits the consensus and complementary properties as well as learns both shared and specific representations by employing the shared and specific representation learning network. Unlike existing shared and specific multi-view representation learning methods that ignore the redundancy problem in representation learning, SMDDRL incorporates the orthogonality and adversarial similarity constraints to reduce the redundancy of learned representations. Moreover, to exploit the information contained in unlabeled data, we design a semi-supervised learning framework by combining deep metric learning and density clustering. Experimental results on three typical multi-view learning tasks, i.e., webpage classification, image classification, and document classification demonstrate the effectiveness of the proposed approach.

When there exist not enough historical defect data for building an accurate prediction model, semisupervised defect prediction (SSDP) and cross-project defect prediction (CPDP) are two feasible solutions. Existing CPDP methods assume that the available source data are well labeled. However, due to expensive human efforts for labeling a large amount of defect data, usually, we can only utilize the suitable unlabeled source data. We call CPDP in this scenario as cross-project semisupervised defect prediction (CSDP). Although some within-project semisupervised defect prediction (WSDP) methods have been developed in recent years, there still exists much room for improvement on prediction performance. In this paper, we aim to provide a unified and effective solution for both CSDP and WSDP problems. We introduce the semisupervised dictionary learning technique and propose a cost-sensitive kernelized semisupervised dictionary learning (CKSDL) approach. CKSDL can make full use of the limited labeled defect data and a large amount of unlabeled data in the kernel space. In addition, CKSDL considers the misclassification costs in the dictionary learning process. Extensive experiments on 16 projects indicate that CKSDL outperforms state-of-the-art WSDP methods, using unlabeled cross-project defect data can help improve the WSDP performance, and CKSDL generally obtains significantly better prediction performance than related SSDP methods in the CSDP scenario.

Person re-identification has been widely studied due to its importance in surveillance and forensics applications. In practice, gallery images are high resolution (HR), while probe images are usually low resolution (LR) in the identification scenarios with large variation of illumination, weather, or quality of cameras. Person re-identification in this kind of scenarios, which we call super-resolution (SR) person re-identification, has not been well studied. In this paper, we propose a semi-coupled low-rank discriminant dictionary learning (SLD ² L) approach for SR person re-identification task. With the HR and LR dictionary pair and mapping matrices learned from the features of HR and LR training images, SLD ² L can convert the features of the LR probe images into HR features. To ensure that the converted features have favorable discriminative capability and the learned dictionaries can well characterize intrinsic feature spaces of the HR and LR images, we design a discriminant term and a low-rank regularization term for SLD ² L. Moreover, considering that low resolution results in different degrees of loss for different types of visual appearance features, we propose a multi-view SLD ² L (MVSLD ² L) approach, which can learn the type-specific dictionary pair and mappings for each type of feature. Experimental results on multiple publicly available data sets demonstrate the effectiveness of our proposed approaches for the SR person re-identification task.