An Adaptive Approach for Fault Localization using R-CNN

Software accuracy and dependability become very important issues now-a-days. It is more difficult to identify software program errors due to the growing size and complexity of programs. Traditional fault localization methods fall short of finding all the defects in a huge real-world program. The use of machine learning in that situation is effective. In this research, we present an R-CNN model-based fault localization technique. The coverage matrix is used as the model's input during training, and it uses test data to determine each program statement's suspiciousness score. Each statement is given a rank based on its score. Three benchmark programs have been looked at to evaluate the proposed work's reliability. The analysis of the results demonstrates that the suggested method can effectively identify false statements.