Techniques for evaluating fault prediction models-Reference-Cited by-同舟云学术

Techniques for evaluating fault prediction models

Published:2008-08-12 Issue:5 Volume:13 Page:561-595
ISSN:1382-3256
Container-title:Empirical Software Engineering
language:en
Short-container-title:Empir Software Eng

Author:

Jiang Yue,Cukic Bojan,Ma Yan

Publisher

Springer Science and Business Media LLC

Subject

Software

Link

http://link.springer.com/content/pdf/10.1007/s10664-008-9079-3.pdf

Reference39 articles.

1. Adams NM, Hand DJ (1999) Comparing classifiers when the misallocation costs are uncertain. Pattern Recognit 32:1139–1147. doi: 10.1016/S0031-3203(98)00154-X

2. Arisholm E, Briand LC (2006) Predicting fault-prone components in a java legacy system. Proceedings of the 2006 ACM/IEEE International Symposium on Empirical Software Engineering (ISESE’06)

3. Azar D, Precup D, Bouktif S, Kegl B, Sahraoui H (2002) Combining and adapting software quality predictive models by genetic algorithms. 17th IEEE International Conference on Automated Software Engineering. IEEE Computer Society

4. Basili VR, Briand LC, Melo WL (1996) A validation of object-oriented design metrics as quality indicators. IEEE Trans Softw Eng 22(10):751–761. doi: 10.1109/32.544352

5. Boetticher GD (2005) Nearest neighbor sampling for better defect prediction. ACM SIGSOFT Software Engineering Notes, 30(4). ACM, New York, NY, pp 1–6

Cited by 173 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Deriving change-prone thresholds from software evolution using ROC curves;The Journal of Supercomputing;2024-07-20

2. Bug numbers matter: An empirical study of effort‐aware defect prediction using class labels versus bug numbers;Software: Practice and Experience;2024-07-10

3. A comparative study of software defect binomial classification prediction models based on machine learning;Software Quality Journal;2024-07-03

4. Mobile Application Online Cross-Project Just-in-Time Software Defect Prediction Framework;ACM Transactions on Software Engineering and Methodology;2024-06-27

5. The untold impact of learning approaches on software fault-proneness predictions: an analysis of temporal aspects;Empirical Software Engineering;2024-06-08