Connecting software metrics across versions to predict defects

作     者：Liu, Yibin Li, Yanhui Guo, Jianbo Zhou, Yuming Xu, Baowen 

作者机构：State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Technology Nanjing University China Institute for Interdisciplinary Information Sciences Tsinghua University Beijing China 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2017年

核心收录：

主　　题：Defects 

摘      要：Accurate software defect prediction could help software practitioners allocate test resources to defect-prone modules effectively and efficiently. In the last decades, much effort has been devoted to build accurate defect prediction models, including developing quality defect predictors and modeling techniques. However, current widely used defect predictors such as code metrics and process metrics could not well describe how software modules change over the project evolution, which we believe is important for defect prediction. In order to deal with this problem, in this paper, we propose to use the Historical Version Sequence of Metrics (HVSM) in continuous software versions as defect predictors. Furthermore, we leverage Recurrent Neural Network (RNN), a popular modeling technique, to take HVSM as the input to build software prediction models. The experimental results show that, in most cases, the proposed HVSM-based RNN model has a significantly better effort-aware ranking effectiveness than the commonly used baseline models. Copyright © 2017, The Authors. All rights reserved.