DEVELOPMENT AND VALIDATION OF HYBRID ALGORITHMS FOR SOFTWARE DEFECT PREDICTION

Abstract

Software defect prediction (SDP) is an important research subject aimed at improving the reliability, maintainability, and overall quality of software systems. The rapid development of software projects raises the need for robust and accurate predictive models. While traditional machine learning (ML) and statistical methods have shown promise for SDP, challenges like high-dimensional data, imbalanced data, inefficient feature selection, and model-tuning limitations persist. To overcome these limita- tions, this research focuses on the development and validation of hybrid algorithms that leverage the power of both machine learning and metaheuristic optimization techniques to improve predictive performance capabilities for SDP. The research is validated through systematic review, empirical studies, and the development of novel algorithms applicable in real-world software development environments. The research is systematically structured into phases, addressing distinct compo- nents of SDP. The initial phase involves a synthesis of a systematic literature review that seeks to evaluate the latest hybrid algorithms that enhance the predictive perfor- mance of SDP models and identify research gaps. The review develops a framework for analyzing the current state-of-the-art with respect to hybrid algorithms on multiple dimensions and highlights the gaps that this thesis will work to address. In subse- quent phases, the research develops and validates several novel hybrid algorithms using benchmark datasets from repositories such as NASA, PROMISE, and AEEEM. These later phases include addressing the prime issues of dataset imbalance, design- ing improved feature selection techniques, implementing hyper-parameter tuning, and evaluating the proposed hybrid models against established baseline methods to demonstrate their effectiveness in real-world software defect prediction scenarios. The high-dimensional software datasets greatly influence the efficiency and ac- curacy of predictive models. Feature selection plays a vital role in simplifying complex datasets while retaining the most significant information. A hybrid SDP model integrating Binary Particle Swarm Optimization (BPSO), Synthetic Minor- ity Oversampling Technique (SMOTE), and Artificial Neural Network (ANN) is proposed to improve software quality. One of the significant contributions of this research is the development of a hybrid defect prediction framework that integrates filter feature selection(Information Gain, Relief F, and Chi-square) and metaheuristic optimization(Opposition-based Whale Optimization Algorithm) for feature selec- tion with attention-based deep learning classifier- Convolutional Neural Networks (1Dimensional- CNN), to achieve higher classification performance. This model is particularly valuable when dealing with large datasets, complex feature interactions, and the need for balancing multiple objectives, such as maximizing classification performance while minimizing the number of features. Predictive models for SDP often underperform when using default configurations, highlighting the critical need for hyperparameter optimization in maximizing model effectiveness. In this research work, we employed advanced optimization techniques, specifically Grey Wolf Optimization (GWO) and Salp Swarm Optimization(SSO) algo- rithms, in combination with machine learning and ensemble classifiers to create more effective hybrid models for SDP. These nature-inspired techniques navigate complex parameter spaces to achieve an effective balance between exploration and exploitation in an optimization process. This study highlights that appropriate hyperparameter tun- ing can yield a significant performance improvement because each predictive model undergoes comprehensive testing for different combinations of parameters before the optimal parameters are reached for each predictive model. Based on the promising outcomes of the hybrid algorithms developed for defect prediction, we further investigate their effectiveness by evaluating various hybrid approaches across multiple datasets to ensure the model 's generalizability. The experimental results are favourable for the hybrid models, which outperform traditional ML and statistical defect prediction models. This superiority is evident across key performance metrics, like F1-score, AUC-ROC, Recall, Precision, G-mean, and MCC. Furthermore, rigorous statistical testing confirms the reliability and robustness of these advanced techniques, reinforcing their effectiveness in SDP. In conclusion, this research significantly progresses the field of SDP by address- ing key predictive modelling challenges through the development and validation of sophisticated hybrid techniques. The study strengthens the effectiveness, reliability, and real-world applicability of defect prediction models. This study offers innovative methods for enhancing software quality, which benefits both academia and industry. The insights generated from this research provide a foundation for future advance- ments in predictive modelling, which will eventually help create software systems that are more dependable, efficient, and free of flaws.