FEDRak: Federated Learning-Based Symmetric Code Statement Ranking Model for Software Fault Forecasting

Abstract

Software Fault Forecasting (SFF) pertains to timely identifying sections in software projects that are prone to faults and may result in significant development expenses. Deep learning models have become widespread in software fault monitoring and management, and these models rely on the design metrics and the code pattern features for classifying the code as erroneous or safe. The proposed model works based on the collective formulation of the fault localization model, which acquires the model-specific metadata for building a global model that would perform software fault forecasting globally. The proposed model works by ranking the suspicious code blocks based on the symmetry of the semantic features of the erroneous code and the implementation code. The feature selection and scaling process is initially performed to precisely identify the features contributing to fault forecasting. The data extraction that is portrayed as the intermediate phase would assist in focusing on the code statements and ranking them based on the impact of the fault. A fine-tuned spectrum-based fault localization technique is used in ranking the statements. The FEDRak model facilitates ongoing adaptation in instances where there are adjustments in the feature contribution of data over time. The federated learning model would update the feature weights of the global model based on the weights synchronized by locally built fault forecasting approaches. FEDRak is statistically analyzed in relation to other contemporary techniques in fault localization in terms of metrics like sensitivity, specificity, accuracy, F1-score, and ROC curves. The proposed model’s performances are analyzed in terms of local and global models.