Leveraging Levy Flight and Greylag Goose Optimization for Enhanced Cross-Project Defect Prediction in Software Evolution

The cross-project defect prediction (CPDP) in software applications is crucial to predict defects and ensure software quality. The performance of the traditional CPDP models is degraded due to the class imbalance issue between different projects and differences in the data distribution. To overcome these limitations, a novel approach is proposed named as Levy flight–enabled greylag goose optimized UniXcoder-based stacked defect predictor (LFGGO-USDP) for the prediction of cross-project defects in the software engineering. In this paper, 23 software projects are selected from diverse datasets such as PROMISE, ReLink, AEEEM, and NASA that are preprocessed for enhancing reliability and reducing class imbalance issues. The transformation model maps source and target projects that are present in the feature space for enhancing predictive performances. During feature selection, the LF mechanism is embedded with the GGO algorithm to localize the features in the source code for enhancing diversity and minimizing local optimum issues. The integration of UniXcoder-based stacked bidirectional long short-term memory (U-SBiLSTM) is implemented as a cross-project defect predictor. The UniXcoder model extracts semantic information for source code tokenization. Then, the output of UniXcoder is fed as input to SBiLSTM, and the SBiLSTM model is applied to determine the relationship between the source code. After that, the output of UniXcoder (which contains the semantic features) is integrated with the output of SBiLSTM (which contains the sequential and temporal dependencies). After concatenating these features, the particular information is selected by using an attention mechanism for categorizing defective and nondefective classes. The experimental investigations are performed to analyze the nondefective and defective cases in software projects and numerical validation is conducted by applying different evaluation models for analyzing the superiority. The proposed model achieved the highest defect prediction accuracy of 0.986 compared to other existing approaches that demonstrates the proposed model provided better prediction outcomes.