Software visualization and deep transfer learning for effective software defect prediction
Jinyin Chen, Keke Hu, Yue Yu, Zhuangzhi Chen, Qi Xuan, Yi Liu, Vladimir Filkov · 2020
Software defect prediction aims to automatically locate defective code modules to better focus testing resources and human effort. Typically, software defect prediction pipelines are comprised of two parts: the first extracts program features, like abstract syntax trees, by using external tools, and the second applies machine learning-based classification models to those features in order to predict defective modules. Since such approaches depend on specific feature extraction tools, machine learning classifiers have to be custom-tailored to effectively build most accurate models.