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Software defect number prediction: Unsupervised vs supervised methods
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-642-21843-9_20openalex-snapshot
A Genetic Algorithm to Configure Support Vector Machines for Predicting Fault-Prone Components
inferredcites →https://doi.org/10.1007/s00500-016-2284-xopenalex-snapshot
An empirical study of some software fault prediction techniques for the number of faults prediction
inferredcites →https://doi.org/10.1007/s10515-010-0069-5openalex-snapshot
Defect prediction from static code features: current results, limitations, new approaches
inferredcites →https://doi.org/10.1007/s10664-008-9080-xopenalex-snapshot
Theory of relative defect proneness
inferredcites →https://doi.org/10.1007/s10664-008-9103-7openalex-snapshot
On the relative value of cross-company and within-company data for defect prediction
inferredcites →https://doi.org/10.1007/s10664-009-9111-2openalex-snapshot
Comparing the effectiveness of several modeling methods for fault prediction
inferredcites →https://doi.org/10.1007/s11219-016-9342-6openalex-snapshot
Tackling class overlap and imbalance problems in software defect prediction
inferredcites →https://doi.org/10.1007/s11390-017-1785-0openalex-snapshot
A Cluster Based Feature Selection Method for Cross-Project Software Defect Prediction
inferredcites →https://doi.org/10.1016/j.eswa.2017.04.014openalex-snapshot
Towards an ensemble based system for predicting the number of software faults
inferredcites →https://doi.org/10.1016/j.infsof.2013.02.009openalex-snapshot
Software fault prediction metrics: A systematic literature review
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.2016.04.017openalex-snapshot
Tuning for software analytics: Is it really necessary?
inferredcites →https://doi.org/10.1016/j.infsof.2017.07.004openalex-snapshot
Which type of metrics are useful to deal with class imbalance in software defect prediction?
inferredcites →https://doi.org/10.1016/j.infsof.2017.08.004openalex-snapshot
MULTI: Multi-objective effort-aware just-in-time software defect prediction
inferredcites →https://doi.org/10.1016/j.ins.2005.12.002openalex-snapshot
Identification of defect-prone classes in telecommunication software systems using design metrics
inferredcites →https://doi.org/10.1016/j.knosys.2016.12.017openalex-snapshot
Linear and non-linear heterogeneous ensemble methods to predict the number of faults in software systems
inferredcites →https://doi.org/10.1023/a:1008202821328openalex-snapshot
Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces
inferredcites →https://doi.org/10.1093/biomet/30.1-2.81openalex-snapshot
A NEW MEASURE OF RANK CORRELATION
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/ase.2015.56openalex-snapshot
CLAMI: Defect Prediction on Unlabeled Datasets (T)
inferredcites →https://doi.org/10.1109/compsac.2014.66openalex-snapshot
FECAR: A Feature Selection Framework for Software Defect Prediction
inferredcites →https://doi.org/10.1109/esem.2017.48openalex-snapshot
File-Level Defect Prediction: Unsupervised vs. Supervised Models
inferredcites →https://doi.org/10.1109/esem.2017.8openalex-snapshot
Code Churn: A Neglected Metric in Effort-Aware Just-in-Time Defect Prediction
inferredcites →https://doi.org/10.1109/icsme.2017.51openalex-snapshot
Supervised vs Unsupervised Models: A Holistic Look at Effort-Aware Just-in-Time Defect Prediction
inferredcites →https://doi.org/10.1109/issre.1991openalex-snapshot
Proceedings. 1991 International Symposium on Software Reliability Engineering
inferredcites →https://doi.org/10.1109/issre.2017.18openalex-snapshot
Learning from Imbalanced Data for Predicting the Number of Software Defects
inferredcites →https://doi.org/10.1109/msr.2017.4openalex-snapshot
The Impact of Using Regression Models to Build Defect Classifiers
inferredcites →https://doi.org/10.1109/saner.2016.56openalex-snapshot
Defect Prediction: Accomplishments and Future Challenges
inferredcites →https://doi.org/10.1109/tr.2007.896761openalex-snapshot
A Comprehensive Empirical Study of Count Models for Software Fault Prediction
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.2015.2461676openalex-snapshot
Empirical Studies of a Two-Stage Data Preprocessing Approach for Software Fault Prediction
inferredcites →https://doi.org/10.1109/tse.2005.49openalex-snapshot
Predicting the location and number of faults in large software systems
inferredcites →https://doi.org/10.1109/tse.2008.90openalex-snapshot
An Investigation into the Functional Form of the Size-Defect Relationship for Software Modules
inferredcites →https://doi.org/10.1109/tse.2011.103openalex-snapshot
A Systematic Literature Review on Fault Prediction Performance in Software Engineering
inferredcites →https://doi.org/10.1109/tse.2012.70openalex-snapshot
A large-scale empirical study of just-in-time quality assurance
inferredcites →https://doi.org/10.1109/tse.2017.2731766openalex-snapshot
MAHAKIL: Diversity Based Oversampling Approach to Alleviate the Class Imbalance Issue in Software Defect Prediction
inferredcites →https://doi.org/10.1109/tse.2017.2770124openalex-snapshot
A Systematic Literature Review and Meta-Analysis on Cross Project Defect Prediction
inferredcites →https://doi.org/10.1109/tse.2018.2794977openalex-snapshot
The Impact of Automated Parameter Optimization on Defect Prediction Models
inferredcites →https://doi.org/10.1109/tse.2018.2876537openalex-snapshot
The Impact of Class Rebalancing Techniques on the Performance and Interpretation of Defect Prediction Models
inferredcites →https://doi.org/10.1111/exsy.12081openalex-snapshot
Resampling strategies for regression
inferredcites →https://doi.org/10.1111/j.2517-6161.1995.tb02031.xopenalex-snapshot
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
inferredcites →https://doi.org/10.1145/1062455.1062558openalex-snapshot
Static analysis tools as early indicators of pre-release defect density
inferredcites →https://doi.org/10.1145/1868328.1868342openalex-snapshot
Towards identifying software project clusters with regard to defect prediction
inferredcites →https://doi.org/10.1145/2372251.2372287openalex-snapshot
Predicting defect numbers based on defect state transition models
inferredcites →https://doi.org/10.1145/2853073.2853083openalex-snapshot
A Decision Tree Regression based Approach for the Number of Software Faults Prediction
inferredcites →https://doi.org/10.1145/2884781.2884839openalex-snapshot
Cross-project defect prediction using a connectivity-based unsupervised classifier
inferredcites →https://doi.org/10.1145/2884781.2884857openalex-snapshot
Automated parameter optimization of classification techniques for defect prediction models
inferredcites →https://doi.org/10.1145/2950290.2950353openalex-snapshot
Effort-aware just-in-time defect prediction: simple unsupervised models could be better than supervised models
inferredcites →https://doi.org/10.1145/3183519openalex-snapshot
Proceedings of the 40th International Conference on Software Engineering: Software Engineering in Practice
inferredcites →https://doi.org/10.1145/3183519.3183547openalex-snapshot
An experience report on defect modelling in practice
inferredcites →https://doi.org/10.1613/jair.953openalex-snapshot
SMOTE: Synthetic Minority Over-sampling Technique
inferredcites →https://doi.org/10.18293/seke2015-132openalex-snapshot
An empirical study on predicting defect numbers
inferredcites →https://doi.org/10.48550/arxiv.1609.02613openalex-snapshot
Why is Differential Evolution Better than Grid Search for Tuning Defect Predictors?
inferredcites →https://doi.org/10.48550/arxiv.1705.03697openalex-snapshot
Is "Better Data" Better than "Better Data Miners"? (On the Benefits of Tuning SMOTE for Defect Prediction)
inferredcites →https://doi.org/10.5555/2819009.2819026openalex-snapshot
Online defect prediction for imbalanced data
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/smr.2234openalex-snapshot
Do different cross‐project defect prediction methods identify the same defective modules?
inferred← citeshttps://doi.org/10.1002/smr.70049openalex-snapshot
UFR‐OSFA: Unified Feature Representation and Oppositional Structure Feature Alignment for Mixed‐Project Heterogeneous Defect Prediction
inferred← citeshttps://doi.org/10.1002/spe.3363openalex-snapshot
Bug numbers matter: An empirical study of effort‐aware defect prediction using class labels versus bug numbers
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Software Testing: A Changing Career
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Predicting the Number of Reported Bugs in a Software Repository
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BugCat: A Novel Approach to Bug Number Categorization with Multi-modal Time Series Learning
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Unsupervised Code Representation Learning via Contrastive Learning for Cross-Project Defect Prediction
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An exploratory analysis of regression methods for predicting faults in software systems
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Software defect prediction model based on improved twin support vector machines
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Survey of software defect prediction features
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A comparative study of handling imbalanced data using generative adversarial networks for machine learning based software fault prediction
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An unsupervised software bug count prediction model based on selected software metrics
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Software defect detection via deep convolutional RBM network and kernel enhanced dragonfly PCA approach
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Optimization of association rules using hybrid data mining technique
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Optimized ensemble machine learning model for software bugs prediction
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SDC-estimator: an effectual software defect count estimation technique for the upcoming version of software project
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DP-Share: Privacy-Preserving Software Defect Prediction Model Sharing Through Differential Privacy
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Linear and non-linear bayesian regression methods for software fault prediction
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The Value of Imbalance Ensemble Techniques on Software Defect Prediction
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DBDNN-Estimator: A Cross-Project Number of Fault Estimation Technique
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An effective approach for CT lung segmentation using mask region-based convolutional neural networks
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Reliable prediction of software defects using Shapley interpretable machine learning models
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A systematic literature review on software defect prediction using artificial intelligence: Datasets, Data Validation Methods, Approaches, and Tools
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BPDET: An effective software bug prediction model using deep representation and ensemble learning techniques
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Machine learning based methods for software fault prediction: A survey
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Prediction of incompatible bug numbers between versions of java open-source software based on deep fusion features
inferred← citeshttps://doi.org/10.1016/j.infsof.2020.106441openalex-snapshot
Revisiting heterogeneous defect prediction methods: How far are we?
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Exploring the software repositories of embedded systems: An industrial experience
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Joint feature representation learning and progressive distribution matching for cross-project defect prediction
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Leveraging developer information for efficient effort-aware bug prediction
inferred← citeshttps://doi.org/10.1016/j.infsof.2021.106652openalex-snapshot
A comprehensive investigation of the impact of feature selection techniques on crashing fault residence prediction models
inferred← citeshttps://doi.org/10.1016/j.infsof.2021.106662openalex-snapshot
Investigation on the stability of SMOTE-based oversampling techniques in software defect prediction
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The impact of the distance metric and measure on SMOTE-based techniques in software defect prediction
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SHSE: A subspace hybrid sampling ensemble method for software defect number prediction
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Predicting the precise number of software defects: Are we there yet?
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Improving effort-aware defect prediction by directly learning to rank software modules
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JIT-CF: Integrating contrastive learning with feature fusion for enhanced just-in-time defect prediction
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An empirical study on pareto based multi-objective feature selection for software defect prediction
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A comprehensive comparative study of clustering-based unsupervised defect prediction models
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GBSR: Graph-based suspiciousness refinement for improving fault localization
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BCV-Predictor: A bug count vector predictor of a successive version of the software system
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DNNAttention: A deep neural network and attention based architecture for cross project defect number prediction
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Ensemble Machine Learning Paradigms in Software Defect Prediction
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Improved software defect prediction using Pruned Histogram-based isolation forest
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Examining the performance of kernel methods for software defect prediction based on support vector machine
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Empirical studies on the impact of filter‐based ranking feature selection on security vulnerability prediction
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OPTIMAL ENERGY MANAGEMENT IN GRID CONNECTED PV BATTERY ENERGY STORAGE SYSTEM WITH MACHINE LEARNING APPROACH
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RETRACTED: An improved software defect prediction model based on grey incidence analysis and Naive Bayes algorithm
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Credibility Based Imbalance Boosting Method for Software Defect Proneness Prediction
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Run, Forest, Run? On Randomization and Reproducibility in Predictive Software Engineering
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