A hybrid one-class rule learning approach based on swarm intelligence for software fault prediction-Reference-Cited by-同舟云学术

A hybrid one-class rule learning approach based on swarm intelligence for software fault prediction

Published:2015-09-18 Issue:4 Volume:11 Page:289-301
ISSN:1614-5046
Container-title:Innovations in Systems and Software Engineering
language:en
Short-container-title:Innovations Syst Softw Eng

Author:

Abdi Yousef^ORCID,Parsa Saeed,Seyfari Yousef

Publisher

Springer Science and Business Media LLC

Subject

Software

Link

http://link.springer.com/content/pdf/10.1007/s11334-015-0258-2.pdf

Reference47 articles.

1. Arisholm E, Briand, LC, Johannessen E (2008) Data mining techniques, candidate measures and evaluation methods for building practically useful fault-proneness prediction models. Dissertation, University of Oslo

2. Anil KJ (2010) Data clustering: 50 years beyond K-means. Pattern Recogn Lett 31(8):651–666

3. de Carvalho AB, Pozo A, Vergilio SR (2010) A symbolic fault prediction model based on multiobjective particle swarm optimization. J Syst Softw 83(5):868–882

4. Catal C (2011) Software fault prediction: a literature review and current trends. Expert Syst Appl 38(4):4626–4636

5. Catal C, Diri B (2009) A systematic review of software fault predictions studies. Expert Syst Appl 36(4):7346–7354

Cited by 21 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. Computing in Cognitive Science Using Ensemble Learning;Modeling and Optimization of Signals Using Machine Learning Techniques;2024-08-23

2. An ensemble meta-estimator to predict source code testability;Applied Soft Computing;2022-11

3. Software fault prediction using data mining, machine learning and deep learning techniques: A systematic literature review;Computers and Electrical Engineering;2022-05

4. Stochastic configuration networks for imbalanced data classification;International Journal of Machine Learning and Cybernetics;2022-04-29

5. FOFS: Firefly Optimization for Feature Selection to Predict Fault-Prone Software Modules;Data Engineering for Smart Systems;2021-11-14