Applying Machine Learning to Predict Software Fault Proneness Using Change Metrics, Static Code Metrics, and a Combination of Them-Reference-Cited by-同舟云学术

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Applying Machine Learning to Predict Software Fault Proneness Using Change Metrics, Static Code Metrics, and a Combination of Them

Published:2018-04 Issue: Volume: Page:
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Container-title:SoutheastCon 2018
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Author:

Alshehri Yasser Ali,Goseva-Popstojanova Katerina,Dzielski Dale G.,Devine Thomas

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/8469205/8478848/08478911.pdf?arnumber=8478911

Cited by 8 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. The untold impact of learning approaches on software fault-proneness predictions: an analysis of temporal aspects;Empirical Software Engineering;2024-06-08

2. Evolutionary measures and their correlations with the performance of cross‐version defect prediction for object‐oriented projects;Journal of Software: Evolution and Process;2023-10-15

3. Intelligent Software Bug Prediction: An Empirical Approach;2023 3rd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST);2023-01-07

4. Use of Support Vector Machine to Check Whether Process Metrics are as Good as Static Code Metrics;Topical Drifts in Intelligent Computing;2022

5. EGIA: A new node splitting method for decision tree generation: Special application in software fault prediction;Materials Today: Proceedings;2021-06

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