Software Defect Prediction via Code Grayscale Pixel Visualization with Fusion Attention (S)
Shaojian Qiu, Shaosheng Wang, Wei Rong, Lili Liao, Yi-Shen Lin · Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering · 2024
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.