Cost-Sensitive Hypergraph Learning With Structure Quality Preservation For IoT Software Defect Prediction

Generative AI is revolutionizing Software Engineering (SE), as both engineers and academics embrace this technology in their work. To better leverage this technology for software generation, it is essential to propose effective IoT software defect prediction methods. However, this task is challenging due to the unclear high-order correlation underlying the data. Moreover, in real-world IoT software defect prediction applications, different types of misclassifications generally lead to distinct losses and associated costs. However, accurately determining these specific costs is often not feasible. Under such circumstances, we propose a cost-sensitive hypergraph learning method with structure quality preservation (csHLQ) to optimize the cost information and preserve the graph quality in a principled way. Due to the representational ability on high-order relationship exploring, we employ hypergraph structure instead of graph structure to model the complex correlations among the datasets. We note that if a cost-sensitive hypergraph has a high quality, its classification results may exhibit a large margin separation. Thus, csHLQ exploits the large margin cost-sensitive hypergraph while avoiding using of a cost-sensitive hypergraph with a small margin. To measure the performance of our proposed method, we performed experiments on three distinct groups of datasets, i.e., the NASA Metrics Data Program (NASA) dataset, CK metric dataset and UCI Machine Learning Repository (UCI) dataset. Experimental results and comparisons with state-of-the-art methods demonstrate the superiority of our method.