Improved software fault prediction using new code metrics and machine learning algorithms

Abstract

Many code metrics exist for bug prediction. However, these metrics are based on the trivial count of code properties and are not sufficient. This research article proposes three new code metrics based on class complexity, coupling, and cohesion to fill the gap. The Promise repository metrics suite's complexity, coupling, and cohesion metrics are replaced by the proposed metrics, and a new metric suite is generated. Experiments show that the proposed metrics suite gives more than 2 % improvement in AUC and precision and approximately 1.5 % in f1-score and recall with fewer code metrics than the existing metrics suite.