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Which type of metrics are useful to deal with class imbalance in software defect prediction?
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/9781118646106.ch6openalex-snapshot
Class Imbalance and Active Learning
inferredcites →https://doi.org/10.1007/11908562_9openalex-snapshot
Investigating Suitability of Software Process and Metrics for Statistical Process Control
inferredcites →https://doi.org/10.1007/3-540-45014-9_1openalex-snapshot
Ensemble Methods in Machine Learning
inferredcites →https://doi.org/10.1007/978-3-642-17229-8openalex-snapshot
Applied Multivariate Statistical Analysis
inferredcites →https://doi.org/10.1007/978-3-642-17229-8_10openalex-snapshot
Principal Components Analysis
inferredcites →https://doi.org/10.1007/s10489-014-0610-5openalex-snapshot
A dissimilarity-based imbalance data classification algorithm
inferredcites →https://doi.org/10.1007/s10515-015-0179-1openalex-snapshot
Multiple kernel ensemble learning for software defect prediction
inferredcites →https://doi.org/10.1007/s11219-014-9241-7openalex-snapshot
Which process metrics can significantly improve defect prediction models? An empirical study
inferredcites →https://doi.org/10.1016/j.asoc.2016.08.002openalex-snapshot
How repeated data points affect bug prediction performance: A case study
inferredcites →https://doi.org/10.1016/j.eswa.2007.11.051openalex-snapshot
A novel hybrid intelligent method based on C4.5 decision tree classifier and one-against-all approach for multi-class classification problems
inferredcites →https://doi.org/10.1016/j.infsof.2009.11.010openalex-snapshot
Exploring the relationship of a file’s history and its fault-proneness: An empirical method and its application to open source programs
inferredcites →https://doi.org/10.1016/j.infsof.2011.09.007openalex-snapshot
Transfer learning for cross-company software defect prediction
inferredcites →https://doi.org/10.1016/j.infsof.2013.05.002openalex-snapshot
A study of subgroup discovery approaches for defect prediction
inferredcites →https://doi.org/10.1016/j.infsof.2014.07.005openalex-snapshot
Software defect prediction using ensemble learning on selected features
inferredcites →https://doi.org/10.1016/j.infsof.2014.11.006openalex-snapshot
An empirical study on software defect prediction with a simplified metric set
inferredcites →https://doi.org/10.1016/j.infsof.2014.12.006openalex-snapshot
ELBlocker: Predicting blocking bugs with ensemble imbalance learning
inferredcites →https://doi.org/10.1016/j.infsof.2015.01.013openalex-snapshot
On the usefulness of ownership metrics in open-source software projects
inferredcites →https://doi.org/10.1016/j.ins.2008.12.001openalex-snapshot
Investigating the effect of dataset size, metrics sets, and feature selection techniques on software fault prediction problem
inferredcites →https://doi.org/10.1016/j.is.2015.02.006openalex-snapshot
Software defect prediction using a cost sensitive decision forest and voting, and a potential solution to the class imbalance problem
inferredcites →https://doi.org/10.1016/j.jss.2007.07.040openalex-snapshot
Predicting defect-prone software modules using support vector machines
inferredcites →https://doi.org/10.1016/j.neucom.2016.02.006openalex-snapshot
A new sampling method for classifying imbalanced data based on support vector machine ensemble
inferredcites →https://doi.org/10.1016/j.patcog.2014.11.014openalex-snapshot
A novel ensemble method for classifying imbalanced data
inferredcites →https://doi.org/10.1016/j.patrec.2014.11.017openalex-snapshot
PCA-guided search for K-means
inferredcites →https://doi.org/10.1016/j.simpat.2012.05.013openalex-snapshot
Formal verification and validation with DEVS-Suite: OSPF Case study
inferredcites →https://doi.org/10.1023/a:1007515423169openalex-snapshot
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants
inferredcites →https://doi.org/10.1023/a:1025316301168openalex-snapshot
Analogy-Based Practical Classification Rules for Software Quality Estimation
inferredcites →https://doi.org/10.1109/32.295895openalex-snapshot
A metrics suite for object oriented design
inferredcites →https://doi.org/10.1109/32.859533openalex-snapshot
Predicting fault incidence using software change history
inferredcites →https://doi.org/10.1109/ase.2013.6693087openalex-snapshot
Personalized defect prediction
inferredcites →https://doi.org/10.1109/icmla.2012.183openalex-snapshot
Evaluation of SMOTE for High-Dimensional Class-Imbalanced Microarray Data
inferredcites →https://doi.org/10.1109/icrito.2015.7359220openalex-snapshot
An investigation of the accuracy of code and process metrics for defect prediction of mobile applications
inferredcites →https://doi.org/10.1109/icsm.2012.6405306openalex-snapshot
Triaging incoming change requests: Bug or commit history, or code authorship?
inferredcites →https://doi.org/10.1109/ictai.2010.27openalex-snapshot
Attribute Selection and Imbalanced Data: Problems in Software Defect Prediction
inferredcites →https://doi.org/10.1109/imsccs.2006.260openalex-snapshot
Research on CMMI-based Software Process Metrics
inferredcites →https://doi.org/10.1109/msr.2010.5463279openalex-snapshot
An extensive comparison of bug prediction approaches
inferredcites →https://doi.org/10.1109/nafips.2007.383813openalex-snapshot
Applying Novel Resampling Strategies To Software Defect Prediction
inferredcites →https://doi.org/10.1109/promise.2007.14openalex-snapshot
Using Developer Information as a Factor for Fault Prediction
inferredcites →https://doi.org/10.1109/seaa.2008.52openalex-snapshot
Software Defect Prediction Using Call Graph Based Ranking (CGBR) Framework
inferredcites →https://doi.org/10.1109/tim.2004.834070openalex-snapshot
PCA-Based Feature Selection Scheme for Machine Defect Classification
inferredcites →https://doi.org/10.1109/tr.2013.2259203openalex-snapshot
Using Class Imbalance Learning for Software Defect Prediction
inferredcites →https://doi.org/10.1109/tr.2014.2370891openalex-snapshot
A Learning-to-Rank Approach to Software Defect Prediction
inferredcites →https://doi.org/10.1109/tse.2007.256941openalex-snapshot
Data Mining Static Code Attributes to Learn Defect Predictors
inferredcites →https://doi.org/10.1109/tsmc.2016.2521840openalex-snapshot
Fuzzy Rule-Based Approach for Software Fault Prediction
inferredcites →https://doi.org/10.1109/tsmcc.2012.2226152openalex-snapshot
Using Coding-Based Ensemble Learning to Improve Software Defect Prediction
inferredcites →https://doi.org/10.1111/j.2517-6161.1958.tb00278.xopenalex-snapshot
Fiducial Distributions and Bayes' Theorem
inferredcites →https://doi.org/10.1137/1.9781611973105openalex-snapshot
Proceedings of the Twenty-Fourth Annual ACM-SIAM Symposium on Discrete Algorithms
inferredcites →https://doi.org/10.1142/s0218194012400037openalex-snapshot
RESAMPLING METHODS IN SOFTWARE QUALITY CLASSIFICATION
inferredcites →https://doi.org/10.1142/s0218194012400116openalex-snapshot
DEFECT PREDICTION USING CASE-BASED REASONING: AN ATTRIBUTE WEIGHTING TECHNIQUE BASED UPON SENSITIVITY ANALYSIS IN NEURAL NETWORKS
inferredcites →https://doi.org/10.1142/s0218539399000292openalex-snapshot
LOGISTIC REGRESSION MODELING OF SOFTWARE QUALITY
inferredcites →https://doi.org/10.1142/s1469026813400014openalex-snapshot
ONLINE CLASS IMBALANCE LEARNING AND ITS APPLICATIONS IN FAULT DETECTION
inferredcites →https://doi.org/10.1145/1062455.1062558openalex-snapshot
Static analysis tools as early indicators of pre-release defect density
inferredcites →https://doi.org/10.1145/1137983.1138012openalex-snapshot
Predicting defect densities in source code files with decision tree learners
inferredcites →https://doi.org/10.1145/1414004openalex-snapshot
Proceedings of the Second ACM-IEEE international symposium on Empirical software engineering and measurement
inferredcites →https://doi.org/10.1145/1414004.1414066openalex-snapshot
Ensemble of software defect predictors
inferredcites →https://doi.org/10.1145/2025113openalex-snapshot
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
inferredcites →https://doi.org/10.1145/2597073openalex-snapshot
Proceedings of the 11th Working Conference on Mining Software Repositories
inferredcites →https://doi.org/10.1145/2597073.2597076openalex-snapshot
The impact of code review coverage and code review participation on software quality: a case study of the qt, VTK, and ITK projects
inferredcites →https://doi.org/10.1145/2601248.2601294openalex-snapshot
Preliminary comparison of techniques for dealing with imbalance in software defect prediction
inferredcites →https://doi.org/10.1145/2786805openalex-snapshot
Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering
inferredcites →https://doi.org/10.1145/2786805.2786850openalex-snapshot
Quality and productivity outcomes relating to continuous integration in GitHub
inferredcites →https://doi.org/10.1145/2810146.2810150openalex-snapshot
What is the Impact of Imbalance on Software Defect Prediction Performance?
inferredcites →https://doi.org/10.1145/2972958openalex-snapshot
Proceedings of the The 12th International Conference on Predictive Models and Data Analytics in Software Engineering
inferredcites →https://doi.org/10.1613/jair.614openalex-snapshot
Popular Ensemble Methods: An Empirical Study
inferredcites →https://doi.org/10.1613/jair.953openalex-snapshot
SMOTE: Synthetic Minority Over-sampling Technique
inferredcites →https://doi.org/10.5167/uzh-55799openalex-snapshot
Don't touch my code! Examining the effects of ownership on software quality
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/s00500-024-10378-xopenalex-snapshot
An adaptive synthetic sampling and batch generation-oriented hybrid approach for addressing class imbalance problem in software defect prediction
inferred← citeshttps://doi.org/10.1007/s00521-020-05570-7openalex-snapshot
A novel ensemble method for classification in imbalanced datasets using split balancing technique based on instance hardness (sBal_IH)
inferred← citeshttps://doi.org/10.1007/s00521-024-10937-1openalex-snapshot
Survey of software defect prediction features
inferred← citeshttps://doi.org/10.1007/s10115-026-02696-3openalex-snapshot
A transformer-based stacked ensemble framework for software defect prediction
inferred← citeshttps://doi.org/10.1007/s10586-023-04170-zopenalex-snapshot
Software defect prediction using a bidirectional LSTM network combined with oversampling techniques
inferred← citeshttps://doi.org/10.1007/s10668-023-04440-1openalex-snapshot
Agricultural land suitability classification and crop suggestion using machine learning and spatial multicriteria decision analysis in semi-arid ecosystem
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A novel approach for software defect prediction using CNN and GRU based on SMOTE Tomek method
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DP-Share: Privacy-Preserving Software Defect Prediction Model Sharing Through Differential Privacy
inferred← citeshttps://doi.org/10.1007/s13198-019-00894-7openalex-snapshot
Multi-response optimization based on desirability function and Taguchi method in agile software development
inferred← citeshttps://doi.org/10.1007/s13198-023-02031-xopenalex-snapshot
Diversity based multi-cluster over sampling approach to alleviate the class imbalance problem in software defect prediction
inferred← citeshttps://doi.org/10.1016/j.asoc.2020.106163openalex-snapshot
Collaborative filtering based recommendation of sampling methods for software defect prediction
inferred← citeshttps://doi.org/10.1016/j.eswa.2023.121251openalex-snapshot
Parameter tuning for software fault prediction with different variants of differential evolution
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Software defect number prediction: Unsupervised vs supervised methods
inferred← citeshttps://doi.org/10.1016/j.infsof.2020.106432openalex-snapshot
COSTE: Complexity-based OverSampling TEchnique to alleviate the class imbalance problem in software defect prediction
inferred← citeshttps://doi.org/10.1016/j.jss.2019.03.012openalex-snapshot
An empirical study on pareto based multi-objective feature selection for software defect prediction
inferred← citeshttps://doi.org/10.1016/j.ress.2020.107170openalex-snapshot
Improved software defect prediction using Pruned Histogram-based isolation forest
inferred← citeshttps://doi.org/10.1016/j.rineng.2025.106578openalex-snapshot
A systematic review on AI based class imbalance handling in software defect prediction
inferred← citeshttps://doi.org/10.1080/08874417.2023.2187483openalex-snapshot
Addressing Class Imbalances in Software Defect Detection
inferred← citeshttps://doi.org/10.1108/ijqrm-07-2019-0235openalex-snapshot
Quality and reliability studies in software defect management: a literature review
inferred← citeshttps://doi.org/10.1109/access.2019.2934530openalex-snapshot
Automatic Feature Exploration and an Application in Defect Prediction
inferred← citeshttps://doi.org/10.1109/ase56229.2023.00157openalex-snapshot
The Devil is in the Tails: How Long-Tailed Code Distributions Impact Large Language Models
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Software defect detection by using data mining based fuzzy logic
inferred← citeshttps://doi.org/10.1109/iccss53909.2021.9721979openalex-snapshot
Kernel-based Class-specific Broad Learning System for software defect prediction
inferred← citeshttps://doi.org/10.1109/icic60109.2023.10382016openalex-snapshot
A Survey on Feature Selection in Imbalanced Data for Software Defect Prediction
inferred← citeshttps://doi.org/10.1109/icse43902.2021.00050openalex-snapshot
Early Life Cycle Software Defect Prediction. Why? How?
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An efficient dual ensemble software defect prediction method with neural network
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A Novel Software Defect Prediction Method Based on Isolation Forest
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Software Defect Prediction Approach Based on a Diversity Ensemble Combined With Neural Network
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DRICP: Defect Risk Identification Using Sample Category Perception
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Eliminating the high false‐positive rate in defect prediction through BayesNet with adjustable weight
inferred← citeshttps://doi.org/10.1155/2020/8852705openalex-snapshot
Software Defect Prediction Based on Fuzzy Weighted Extreme Learning Machine with Relative Density Information
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An Empirical Study of Software Metrics Diversity for Cross-Project Defect Prediction
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Accuracy, precision, recall, f1-score, or MCC? empirical evidence from advanced statistics, ML, and XAI for evaluating business predictive models
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A Novel Approach for Software Defect Prediction using CNN and GRU Based on SMOTE Tomek Method
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A Novel 4-C Model For Multilabel Classification Of Software Defects
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Adaptive 1-D CNN Using LSelect Feature Selection for Predicting Software Faults
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Early Life Cycle Software Defect Prediction. Why? How?
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