Cross-Project Transfer Learning on Lightweight Code Semantic Graphs for Defect Prediction

Author:

Fang Dingbang1ORCID,Liu Shaoying1,Li Yang1

Affiliation:

1. Graduate School of Advanced Science and Engineering, Hiroshima University, Hiroshima 739-8527, Japan

Abstract

A deep learning system (DLS) developed based on one software project for defect prediction may well be applied to the related code on the same project but is usually difficult to be applied to new or unknown software projects. To address this problem, we propose a Transferable Graph Convolutional Neural Network (TGCNN) that can learn defects from the lightweight semantic graphs of code and transfer the learned knowledge from the source project to the target project. We discuss how the semantic graph is constructed from code; how the TGCNN can learn from the graph; and how the learned knowledge can be transferred to a new or unknown project. We also conduct a controlled experiment to evaluate our method. The result shows that despite some limitations, our method performs considerably better than existing methods.

Publisher

World Scientific Pub Co Pte Ltd

Subject

Artificial Intelligence,Computer Graphics and Computer-Aided Design,Computer Networks and Communications,Software

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