Software defect prediction (SDP) is a crucial phase preceding the launch of software products. Cross‐project defect prediction (CPDP) is introduced for the anticipation of defects in novel projects lacking defect labels. CPDP can use defect information of mature projects to speed up defect prediction for new projects. So that developers can quickly get the defect information of the new project, so that they can test the software project pertinently. At present, the predominant approaches in CPDP rely on deep learning, and the performance of the ultimate model is notably affected by the quality of the training dataset. However, the dataset of CPDP not only has few samples but also has almost no label information in new projects, which makes the general deep‐learning‐based CPDP model not ideal. In addition, most of the current CPDP models do not fully consider the enrichment of classification boundary samples after cross‐domain, leading to suboptimal predictive capabilities of the model. To overcome these obstacles, we present contrastive learning pretraining for CPDP (ConCPDP), a CPDP method integrating contrastive pretraining and category boundary adjustment. We first perform data augmentation on the source and target domain code files and then extract the enhanced data as an abstract syntax tree (AST). The AST is then transformed into an integer sequence using specific mapping rules, serving as input for the subsequent neural network. A neural network based on bidirectional long short‐term memory (Bi‐LSTM) will receive an integer sequence and output a feature vector. Then, the feature vectors are input into the contrastive module to optimise the feature extraction network. The pretrained feature extractor can be fine‐tuned by the maximum mean discrepancy (MMD) between the feature distribution of the source domain and the target domain and the binary classification loss on the source domain. This paper conducts a large number of experiments on the PROMISE dataset, which is commonly used for CPDP, to validate ConCPDP’s efficacy, achieving superior results in terms of F 1 measure, area under curve (AUC), and Matthew’s correlation coefficient (MCC).

As mobile applications evolve rapidly, their fast iterative update nature leads to an increase in software defects. Just-In-Time Software Defect Prediction (JIT-SDP) offers immediate feedback on code changes. For new applications without historical data, researchers have proposed Cross-Project JIT-SDP (CP JIT-SDP). Existing CP JIT-SDP approaches are designed for offline scenarios where target data is available in advance. However, target data in real-world applications usually arrives online in a streaming manner, making online CP JIT-SDP face cross-project distribution differences and target project data concept drift challenges in online scenarios. These challenges often co-exist during application development, and their interactions cause model performance to degrade. To address these issues, we propose an online CP JIT-SDP framework called COTL. Specifically, COTL consists of two stages: offline and online. In the offline stage, the cross-domain structure preserving projection algorithm is used to reduce the cross-project distribution differences. In the online stage, target data arrives sequentially over time. By reducing the differences in marginal and conditional distributions between offline and online data for target project, concept drift is mitigated and classifier weights are updated online. Experimental results on 15 mobile application benchmark datasets show that COTL outperforms 13 benchmark methods on four performance metrics.

Abstract Cross‐project defect prediction (CPDP) is an important research direction in software defect prediction. Traditional CPDP methods based on hand‐crafted features ignore the semantic information in the source code. Existing CPDP methods based on the deep learning model may not fully consider the differences among projects. Additionally, these methods may not accurately classify the samples near the classification boundary. To solve these problems, the authors propose a model based on multi‐adaptation and nuclear norm (MANN) to deal with samples in projects. The feature of samples were embedded into the multi‐core Hilbert space for distribution and the multi‐kernel maximum mean discrepancy method was utilised to reduce differences among projects. More importantly, the nuclear norm module was constructed, which improved the discriminability and diversity of the target sample by calculating and maximizing the nuclear norm of the target sample in the process of domain adaptation, thus improving the performance of MANN. Finally, extensive experiments were conducted on 11 sizeable open‐source projects. The results indicate that the proposed method exceeds the state of the art under the widely used metrics.