Enhanced Software Fault Prediction by Analyzing Code Comments Using CodeBERT

Software Fault Prediction (SFP) models help to improve source code quality by reducing the logical mistakes inside the source code. This work proposed the framework based on metrics and code embedding with the help of CODEBERT(CB) methods extracted from source code. We have used CODEBERT(CB) to extract numerical features from code and comments. The proposed framework has been validated with the help of the most frequently used machine learning classifier on 41 different Java open-source datasets. The final finding of the trained machine learning models confirms that the proposed approach effectively identifies faulty source code with an accuracy of 80.39% and an area under the curve of 0.7458.