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Automated change-prone class prediction on unlabeled dataset using unsupervised method
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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/smr.1549openalex-snapshot
A suite of metrics for quantifying historical changes to predict future change‐prone classes in object‐oriented software
inferredcites →https://doi.org/10.1007/s00500-014-1576-2openalex-snapshot
Three empirical studies on predicting software maintainability using ensemble methods
inferredcites →https://doi.org/10.1007/s10515-011-0090-3openalex-snapshot
An investigation on the feasibility of cross-project defect prediction
inferredcites →https://doi.org/10.1007/s10515-016-0203-0openalex-snapshot
An exploratory study for software change prediction in object-oriented systems using hybridized techniques
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/s10664-010-9148-2openalex-snapshot
An empirical study on the influence of pattern roles on change-proneness
inferredcites →https://doi.org/10.1007/s10664-011-9162-zopenalex-snapshot
Calculation and optimization of thresholds for sets of software metrics
inferredcites →https://doi.org/10.1007/s10664-011-9170-zopenalex-snapshot
The ability of object-oriented metrics to predict change-proneness: a meta-analysis
inferredcites →https://doi.org/10.1007/s10664-011-9182-8openalex-snapshot
On the dataset shift problem in software engineering prediction models
inferredcites →https://doi.org/10.1016/0164-1212(93)90077-bopenalex-snapshot
Object-oriented metrics that predict maintainability
inferredcites →https://doi.org/10.1016/j.infsof.2005.03.002openalex-snapshot
An application of Bayesian network for predicting object-oriented software maintainability
inferredcites →https://doi.org/10.1016/j.infsof.2014.05.017openalex-snapshot
Automated classification of software change messages by semi-supervised Latent Dirichlet Allocation
inferredcites →https://doi.org/10.1016/j.infsof.2016.01.005openalex-snapshot
A component recommender for bug reports using Discriminative Probability Latent Semantic Analysis
inferredcites →https://doi.org/10.1016/j.jss.2006.05.017openalex-snapshot
Identifying and characterizing change-prone classes in two large-scale open-source products
inferredcites →https://doi.org/10.1016/j.jss.2006.10.049openalex-snapshot
Predicting object-oriented software maintainability using multivariate adaptive regression splines
inferredcites →https://doi.org/10.1016/j.jss.2008.06.001openalex-snapshot
A study of project selection and feature weighting for analogy based software cost estimation
inferredcites →https://doi.org/10.1016/j.jss.2015.12.019openalex-snapshot
Automatically classifying software changes via discriminative topic model: Supporting multi-category and cross-project
inferredcites →https://doi.org/10.1109/32.295895openalex-snapshot
A metrics suite for object oriented design
inferredcites →https://doi.org/10.1109/apsec.2004.99openalex-snapshot
Proceedings. 11th Asia-Pacific Software Engineering Conference
inferredcites →https://doi.org/10.1109/ase.2015.56openalex-snapshot
CLAMI: Defect Prediction on Unlabeled Datasets (T)
inferredcites →https://doi.org/10.1109/csmr-wcre.2014.6747166openalex-snapshot
Cross-project defect prediction models: L'Union fait la force
inferredcites →https://doi.org/10.1109/csmr.2009.57openalex-snapshot
Application of TreeNet in Predicting Object-Oriented Software Maintainability: A Comparative Study
inferredcites →https://doi.org/10.1109/csmr.2013.1openalex-snapshot
Proceedings of the 2013 17th European Conference on Software Maintenance and Reengineering (CSMR 2013)
inferredcites →https://doi.org/10.1109/hase.2004.1281739openalex-snapshot
Unsupervised learning for expert-based software quality estimation
inferredcites →https://doi.org/10.1109/hpcsim.2014.6903747openalex-snapshot
Examining the effectiveness of machine learning algorithms for prediction of change prone classes
inferredcites →https://doi.org/10.1109/icacci.2014.6968347openalex-snapshot
Cross project change prediction using open source projects
inferredcites →https://doi.org/10.1109/icacci.2015.7275614openalex-snapshot
Mining the impact of object oriented metrics for change prediction using Machine Learning and Search-based techniques
inferredcites →https://doi.org/10.1109/icse.2004openalex-snapshot
Proceedings. 26th International Conference on Software Engineering
inferredcites →https://doi.org/10.1109/icstw.2011.43openalex-snapshot
An Empirical Study on Object-Oriented Metrics and Software Evolution in Order to Reduce Testing Costs by Predicting Change-Prone Classes
inferredcites →https://doi.org/10.1109/msr.2013.6624057openalex-snapshot
Better cross company defect prediction
inferredcites →https://doi.org/10.1109/tse.2002.1019484openalex-snapshot
Assessing the applicability of fault-proneness models across object-oriented software projects
inferredcites →https://doi.org/10.1109/tse.2005.89openalex-snapshot
Comparing high-change modules and modules with the highest measurement values in two large-scale open-source products
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/tse.2009.32openalex-snapshot
Examining the Potentially Confounding Effect of Class Size on the Associations between Object-Oriented Metrics and Change-Proneness
inferredcites →https://doi.org/10.1109/tse.2012.88openalex-snapshot
Active learning and effort estimation: Finding the essential content of software effort estimation data
inferredcites →https://doi.org/10.1109/wcre.2009.28openalex-snapshot
An Exploratory Study of the Impact of Code Smells on Software Change-proneness
inferredcites →https://doi.org/10.1142/s0218194009004489openalex-snapshot
TEMPORAL SOFTWARE CHANGE PREDICTION USING NEURAL NETWORKS
inferredcites →https://doi.org/10.1145/1595696openalex-snapshot
Proceedings of the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
inferredcites →https://doi.org/10.1145/2345396openalex-snapshot
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
inferredcites →https://doi.org/10.1145/2884781.2884839openalex-snapshot
Cross-project defect prediction using a connectivity-based unsupervised classifier
inferredcites →https://doi.org/10.1145/2950290openalex-snapshot
Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering
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/10.1145/3324884openalex-snapshot
Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering
inferredcites →https://doi.org/10.13189/csit.2014.020103openalex-snapshot
Support Vector Machine and Least Square Support Vector Machine Stock Forecasting Models
inferredcites →https://doi.org/10.18293/seke2016-039openalex-snapshot
Self-learning Change-prone Class Prediction
inferredcites →https://doi.org/10.2307/2346830openalex-snapshot
Algorithm AS 136: A K-Means Clustering Algorithm
inferredcites →https://doi.org/10.5167/uzh-25785openalex-snapshot
Cross-project defect prediction: a large scale experiment on data vs. domain vs. process
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.2445openalex-snapshot
Simplified abstract syntax tree based semantic features learning for software change prediction
inferred← citeshttps://doi.org/10.1002/smr.2660openalex-snapshot
An assessment of heterogenous ensemble classifiers for analyzing change‐proneness in open‐source software systems
inferred← citeshttps://doi.org/10.1007/978-3-030-58802-1_25openalex-snapshot
Software Defect Prediction on Unlabelled Datasets: A Comparative Study
inferred← citeshttps://doi.org/10.1007/s00500-025-10751-4openalex-snapshot
Explainable-AI-assisted feature selection for software change-proneness prediction
inferred← citeshttps://doi.org/10.1007/s10489-025-06557-4openalex-snapshot
An unsupervised software bug count prediction model based on selected software metrics
inferred← citeshttps://doi.org/10.1007/s11219-020-09546-7openalex-snapshot
An improved text classification modelling approach to identify security messages in heterogeneous projects
inferred← citeshttps://doi.org/10.1007/s11227-024-06366-5openalex-snapshot
Deriving change-prone thresholds from software evolution using ROC curves
inferred← citeshttps://doi.org/10.1007/s13369-026-11366-zopenalex-snapshot
Unsupervised Software Defect Prediction in Haskell: A Metrics-Driven Ensemble Approach Using Function-Level Thresholding
inferred← citeshttps://doi.org/10.1016/j.infsof.2019.01.010openalex-snapshot
State of the art in hybrid strategies for context reasoning: A systematic literature review
inferred← citeshttps://doi.org/10.1051/epjconf/202024505041openalex-snapshot
Lessons Learned from the Assessment of Software Defect Prediction on WLCG Software: A Study with Unlabelled Datasets and Machine Learning Techniques
inferred← citeshttps://doi.org/10.1109/compsac.2018.00017openalex-snapshot
Cross-Project Change-Proneness Prediction
inferred← citeshttps://doi.org/10.1109/icac3n56670.2022.10074366openalex-snapshot
Change Prediction using Selective Cross Project Validation with Ensemble Learning
inferred← citeshttps://doi.org/10.1109/icitiit54346.2022.9744186openalex-snapshot
Cross-Project Change-Proneness Prediction with Selected Source Project
inferred← citeshttps://doi.org/10.1109/icse.2019.00024openalex-snapshot
LEOPARD: Identifying Vulnerable Code for Vulnerability Assessment Through Program Metrics
inferred← citeshttps://doi.org/10.1109/nss/mic42677.2020.9508053openalex-snapshot
A New Code Change Prediction Dataset: A Case Study Based on HEP Software
inferred← citeshttps://doi.org/10.1109/qrs51102.2020.00038openalex-snapshot
How Well Just-In-Time Defect Prediction Techniques Enhance Software Reliability?
inferred← citeshttps://doi.org/10.1109/tr.2022.3151125openalex-snapshot
An Unsupervised Software Fault Prediction Approach Using Threshold Derivation
inferred← citeshttps://doi.org/10.1145/3230467.3230477openalex-snapshot
Identifying Threshold Values of Change-Prone Modules
inferred← citeshttps://doi.org/10.1145/3477535openalex-snapshot
On the Reproducibility and Replicability of Deep Learning in Software Engineering
inferred← citeshttps://doi.org/10.26555/ijain.v6i3.484openalex-snapshot
Improved point center algorithm for K-Means clustering to increase software defect prediction
inferred← citeshttps://doi.org/10.28945/5193openalex-snapshot
Predicting Software Change-Proneness From Software Evolution Using Machine Learning Methods
inferred← citeshttps://doi.org/10.3233/mgs-200321openalex-snapshot
Software defect prediction based on weighted extreme learning machine
inferred← citeshttps://doi.org/10.3390/app122110773openalex-snapshot
Natural Language Processing Application on Commit Messages: A Case Study on HEP Software
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