Abstract: A multiple kernel dictionary learning approach for software defect prediction was proposed.Software historical defect data have complicated structure and marked characteristic of class-imbalance.Multiple kernel learning is an effective technique in the field of machine learning which can map the historical defect data to a higher-dimensional feature space and make them express better.We got a multiple kernel dictionary learning classifier which has the advantages of both multiple kernel learning and dictionary learning.The widely used datasets from NASA MDP datasets are employed as test data to evaluate the performance of all compared methods.Experimental results demonstrate the effectiveness of the proposed multiple kernel dictionary learning approach for the software defect prediction task.

Key words: Software detect prediction,Multiple kernel learning,Dictionary learning,Class-imbalance