Empirical Analysis of Object-Oriented Design Metrics for Predicting High and Low Severity Faults
By Yuming Zhou, Hareton K. N. Leung. IEEE Transactions on Software Engineering, 2006
390 citations recorded by the source corpus.
Subfields: Information Systems, Software
View Paper: https://www.semanticscholar.org/paper/ff350a2edef9a577f6c3c8d372040c60c9d5aea0
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Information Systems
- Ruchika Malhotra
- Richard Torkar
- M. Heričko
- Danijel Radjenovic
- Ales Zivkovic
- Cagatay Catal
- Giancarlo Succi
- Witold Pedrycz
In Software
- Ruchika Malhotra
- Richard Torkar
- M. Heričko
- Danijel Radjenovic
- Ales Zivkovic
- Cagatay Catal
- Giancarlo Succi
- Witold Pedrycz
Citations in the corpus
- Software fault prediction metrics: A systematic literature review (2013, methodology): “Object-oriented metrics are reported to be better at predicting faults than complexity and size metrics [62,66,67,81,149,89, 153,128,132,136,53,137].”
- Software fault prediction: A literature review and current trends (2011, methodology): “Zhou and Leung (2006) predicted high and low severity faults by using object oriented software metrics on NASA’s KC1 dataset.”
- A systematic review of software fault prediction studies (2009, methodology): “According to several software fault prediction studies, CBO, WMC, and RFC are the most significant CK metrics for fault prediction (Zhou & Leung, 2006).”
- A Systematic Literature Review on Fault Prediction Performance in Software Engineering (2012, neutral): “Lamkanfi et al. [24], Singh et al. [[167]] and Zhou and Leung [[202]] are other studies which have also investigated severity.”
- A comparative analysis of the efficiency of change metrics and static code attributes for defect prediction (2008, background): “The former aims at predicting the number of defects present in a software unit whereas the latter usually aims at inferring whether a software unit is defect free or not and hence makes a binary classification (sometimes more than two classes are considered, for example by using the defect severity ”
- A systematic review of machine learning techniques for software fault prediction (2015, background): “Hence, total number of 122 studies [1, 2, 20-139] were identified for further processing and analysis.”
- Software defect prediction using Bayesian networks (2012, methodology): “On the other hand, Zhou and Leung use logistic regression and machine learning methods (naive Bayes network, random forest, and nearest neighbor with generalization) to determine the importance of object oriented metrics for determining fault severity (Zhou and Leung 2006).”