ALOGO: A Novel and Effective Framework for Online Cross-Project Defect Prediction

Cross-project defect prediction (CPDP) uses the historical defect dataset collected from source projects to train a model and then applies it to the target project. However, almost all existing CPDP methods are developed for offline scenarios where the trained models are fixed and cannot be updated along with the incoming labeled target instances after training. Actually, the label of target instances usually arrives online in a streaming manner which can be used to update CPDP models for better defect prediction performance on the next unlabeled target instance. To bridge these gaps, we propose a novel effective online cross-project defect prediction framework named ALOGO. ALOGO includes two essential phases: offline cross-project defect prediction phase and online within-project defect prediction (WPDP) phase which are combined by an adaptive weighted adjustment mechanism. In the offline CPDP phase, the global offline defect knowledge is learned by minimizing the difference between the source and target datasets based on an offline CPDP model. In the online WPDP phase, the local online defect knowledge is learned based on an online WPDP model. These two kinds of defect knowledge are then combined to obtain the latest and most valuable defect knowledge. Experimental results on 27 defect datasets show that ALOGO improves the performance over the existing state-of-the-art online CPDP model by 31.2% in terms of the Matthews correlation coefficient (MCC) and also outperforms the baseline in terms of other four well-known measures. It can be concluded that 1) it is necessary to consider building online CPDP models; 2) ALOGO is a more promising alternative for online CPDP.