Active Learning using Uncertainty Sampling and Query-by-Committee for Software Defect Prediction
Qu Yubin, Xiang Chen, Chen Ruijie, Xiaolin Ju, Guo Jiangfeng · International Journal of Performability Engineering · 2019
In the process of software defect prediction dataset construction, there are problems such as high labeling costs.Active learning can reduce labeling costs when using uncertainty sampling.Samples with the most uncertainty will be labeled, but samples with the highest certainty will always be discarded.According to cognitive theory, easy samples can promote the performance of the model.Therefore, a hybrid active learning query strategy is proposed.For the sample with lowest information entropy, query-by-committee will analyze it again using vote entropy.Empirical studies show that the proposed HIVE approach outperforms several state-of-the-art active learning approaches.