| data unpredictability in software defect-fixing effort prediction | |
| He Zhimin; Shu Fengdi; Yang Ye; Zhang Wen; Wang Qing | |
| 2010 | |
| 会议名称 | 10th International Conference on Quality Software, QSIC 2010 |
| 会议录名称 | Proceedings - International Conference on Quality Software |
| 页码 | 220-226 |
| 会议日期 | 37451 |
| 会议地点 | Zhangjiajie, China |
| 出版地 | United States |
| ISSN | 15506002 |
| ISBN | 9780770000000 |
| 部门归属 | (1) Laboratory for Internet Software Technologies, Institute of Software, Chinese Academy of Sciences, Beijing 100080, China; (2) Graduate University, Chinese Academy of Sciences, Beijing 100039, China |
| 摘要 | The prediction of software defect-fixing effort is important for strategic resource allocation and software quality management. Machine learning techniques have become very popular in addressing this problem and many related prediction models have been proposed. However, almost every model today faces a challenging issue of demonstrating satisfactory prediction accuracy and meaningful prediction results. In this paper, we investigate what makes high-precision prediction of defect-fixing effort so hard from the perspective of the characteristics of defect dataset. We develop a method using a metric to quantitatively analyze the unpredictability of a defect dataset and carry out case studies on two defect datasets. The results show that data unpredictability is a key factor for unsatisfactory prediction accuracy and our approach can explain why high-precision prediction for some defect datasets is hard to achieve inherently. We also provide some suggestions on how to collect highly predictable defect data. © 2010 IEEE. |
| 关键词 | Defects Forecasting Learning Systems Mathematical Models Quality Management |
| 主办者 | National Laboratory for Parallel and Distributed Processing; The University of Hong Kong |
| 内容类型 | 会议论文 |
| URI标识 | http://ir.iscas.ac.cn/handle/311060/8720 |
| 专题 | 互联网软件技术实验室 |
| 推荐引用方式 GB/T 7714 | He Zhimin,Shu Fengdi,Yang Ye,et al. data unpredictability in software defect-fixing effort prediction[C]. United States,2010:220-226. |
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