Software Defect Prediction via Code Grayscale Pixel Visualization with Fusion Attention (S)

Software defect prediction helps quality assurance teams find defects in software, thereby enhancing the reliability of the systems.In existing code-visualization-based defect prediction methods, challenges arise from mixing code information and the potential omission of critical defect features.To enhance the completeness of code features, this paper proposes a defect prediction model based on code grayscale pixel visualization with a fusion attention mechanism (Gpv2DP).Gpv2DP converts code into grayscale images and reshapes the images to a standard size, effectively alleviating the information loss problem caused by element mixing and image cropping.Furthermore, it constructs a code feature extracting network that simultaneously integrates the channel, spatial and 3D attention.We conduct empirical experiments on ten open-source Java projects from the PROMISE repository.The results show that the F-measure and AUC metrics of Gpv2DP outperform related defect prediction methods.