Causally Remove Negative Confound Effects of Size Metric for Software Defect Prediction

Abstract

Software defect prediction technology can effectively detect potential defects in the software system. The most common method is to establish machine learning models based on software metrics for prediction. However, most of the prediction models are proposed without considering the confounding effects of size metric. The size metric has unexpected correlations with other software metrics and introduces biases into prediction results. Suitably removing these confounding effects to improve the prediction model’s performance is an issue that is still largely unexplored. This paper proposes a method that can causally remove the negative confounding effects of size metric. First, we quantify the confounding effects based on a causal graph. Then, we analyze each confounding effect to determine whether they are positive or negative, and only the negative confounding effects are removed. Extensive experimental results on eight data sets demonstrate the effectiveness of our proposed method. The prediction model’s performance can, in general, be improved after removing the negative confounding effects of size metric.

Keywords

• defect prediction
• size metric
• confounding effect
• casual graph
• logistic regression
• generalized additive models