Dealing with Noise in Defect Prediction

Sunghun Kim, Hongyu Zhang, Rongxin Wu, and Liang Gong
Hong Kong University of Science and Technology, China; Tsinghua University, China
Session: 
Empirical Luau II

Many software defect prediction models have been built using historical defect data obtained by mining software repositories (MSR). Recent studies have discovered that data so collected contain noises because current defect collection practices are based on optional bug fix keywords or bug report links in change logs. Automatically collected defect data based on the change logs could include noises. This paper proposes approaches to deal with the noise in defect data. First, we measure the impact of noise on defect prediction models and provide guidelines for acceptable noise level. We measure noise resistant ability of two well-known defect prediction algorithms and find that in general, for large defect datasets, adding FP (false positive) or FN (false negative) noises alone does not lead to substantial performance differences. However, the prediction performance decreases significantly when the dataset contains 20%-35% of both FP and FN noises. Second, we propose a noise detection and elimination algorithm to address this problem. Our empirical study shows that our algorithm can identify noisy instances with reasonable accuracy. In addition, after eliminating the noises using our algorithm, defect prediction accuracy is improved.