Candidate project selection method combined with feature filtering in cross project defect prediction

Cross project defect prediction is a valuable strategy for identifying software defects when historical data is unavailable, yet selecting the right training project for the predictive model is a challenging task. Additionally, dealing with numerous redundant features during model training significantly impacts prediction accuracy. To tackle these challenges, this paper presents a solution that combines candidate project selection with feature filtering. It combines the Collaborative Filtering method with the Content Based method to identify the most appropriate candidate project. For feature filtering, the Pearson feature selection approach is utilized to eliminate data duplication and determine the optimal number of features. Experimental results affirm the effectiveness of this approach, showcasing its superiority to existing approaches in regard to F-score and Area Under the Curve metrics.