Hybrid Sampling and Random Forest Based Machine Learning Approach for Software Defect Prediction-Reference-Cited by-同舟云学术

Hybrid Sampling and Random Forest Based Machine Learning Approach for Software Defect Prediction

Published:2020 Issue: Volume: Page:541-553
ISSN:1876-1100
Container-title:Lecture Notes in Electrical Engineering
language:
Short-container-title:

Author:

Hossen Md Anwar,Islam Md. Shariful,Yusof Nurhafizah Abu Talip,Rahman Md. Sakib,Siddika Fatema,Rahman Mostafijur,Khatun Sabira,Karim Mohamad Shaiful Abdul,Mahmud S. M. Hasan

Publisher

Springer Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-15-2317-5_46

Reference19 articles.

1. Menzies T, Greenwald J, Frank A (2007) Data mining static code attributes to learn defect predictors. IEEE Trans Softw Eng 33:2–13

2. Lin J-C, Wu K-C (2007) Digging high risk defects out in software engineering. In: International conference on intelligent information processing. Springer US, pp 20–23

3. Gray D, Bowes D (2011) The misuse of the NASA metrics data program data sets for automated software defect prediction. In: IET conference proceedings. The Institution of Engineering & Technology, pp 96–103 (2011)

4. Lessmann S, Baesens B (2008) Benchmarking classification models for software defect prediction: a proposed framework and novel findings. IEEE Trans Software Eng 34(4):485–496

5. Khoshgoftaar TM, Gao K, Napolitano A (2012) An empirical study of feature ranking techniques for software quality prediction. Int J Softw Eng Knowl Eng 22:161–183

Cited by 2 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. Software Defect Prediction Approach Based on a Diversity Ensemble Combined With Neural Network;IEEE Transactions on Reliability;2024-09

2. Data Balancing Improves Self-Admitted Technical Debt Detection;2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR);2021-05