Improved software defect prediction using Pruned Histogram-based isolation forest
Author
Abstract
Suggested Citation
DOI: 10.1016/j.ress.2020.107170
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Lee, Sang Hun & Lee, Seung Jun & Shin, Sung Min & Lee, Eun-chan & Kang, Hyun Gook, 2020. "Exhaustive testing of safety-critical software for reactor protection system," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
- Wang, Jinyong & Zhang, Ce, 2018. "Software reliability prediction using a deep learning model based on the RNN encoder–decoder," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 73-82.
- Heydari, Mohammadhossein & Sullivan, Kelly M., 2019. "Robust allocation of testing resources in reliability growth," Reliability Engineering and System Safety, Elsevier, vol. 192(C).
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Gao, Lu & Lu, Pan & Ren, Yihao, 2021. "A deep learning approach for imbalanced crash data in predicting highway-rail grade crossings accidents," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
- Yinsheng Fu & Jullius Kumar & Bibhu Prasad Ganthia & Rahul Neware, 2022. "Nonlinear dynamic measurement method of software reliability based on data mining," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 273-280, March.
- Hugo Núñez Delafuente & César A. Astudillo & David Díaz, 2024. "Ensemble Approach Using k-Partitioned Isolation Forests for the Detection of Stock Market Manipulation," Mathematics, MDPI, vol. 12(9), pages 1-18, April.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Shin, Sung-Min & Lee, Sang Hun & Shin, Seung Ki, 2022. "A novel approach for quantitative importance analysis of safety DI&C systems in the nuclear field," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
- Wang, Peipei & Zheng, Xinqi & Ai, Gang & Liu, Dongya & Zhu, Bangren, 2020. "Time series prediction for the epidemic trends of COVID-19 using the improved LSTM deep learning method: Case studies in Russia, Peru and Iran," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
- Xu, Zhaoyi & Saleh, Joseph Homer, 2021. "Machine learning for reliability engineering and safety applications: Review of current status and future opportunities," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
- Da Hye Lee & In Hong Chang & Hoang Pham, 2020. "Software Reliability Model with Dependent Failures and SPRT," Mathematics, MDPI, vol. 8(8), pages 1-14, August.
- Kyawt Kyawt San & Hironori Washizaki & Yoshiaki Fukazawa & Kiyoshi Honda & Masahiro Taga & Akira Matsuzaki, 2021. "Deep Cross-Project Software Reliability Growth Model Using Project Similarity-Based Clustering," Mathematics, MDPI, vol. 9(22), pages 1-22, November.
- Modibbo, Umar Muhammad & Arshad, Mohd. & Abdalghani, Omer & Ali, Irfan, 2021. "Optimization and estimation in system reliability allocation problem," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
- Dahye Lee & Inhong Chang & Hoang Pham, 2023. "Study of a New Software Reliability Growth Model under Uncertain Operating Environments and Dependent Failures," Mathematics, MDPI, vol. 11(18), pages 1-17, September.
- Li, Xingyu & Krivtsov, Vasiliy & Arora, Karunesh, 2022. "Attention-based deep survival model for time series data," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
- Murray, Brian & Perera, Lokukaluge Prasad, 2021. "An AIS-based deep learning framework for regional ship behavior prediction," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
- Dehghani, Nariman L. & Zamanian, Soroush & Shafieezadeh, Abdollah, 2021. "Adaptive network reliability analysis: Methodology and applications to power grid," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
More about this item
Keywords
Software defect prediction; Software quality and reliability; Isolation forest; Imbalanced data distribution; Ensemble pruning; Histogram;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:204:y:2020:i:c:s0951832020306712. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.