Linear and non-linear bayesian regression methods for software fault prediction
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[1] Hossein Abbasimehr,et al. Improving time series forecasting using LSTM and attention models , 2021, Journal of Ambient Intelligence and Humanized Computing.
[2] Hoa Khanh Dam,et al. An Empirical Study of Model-Agnostic Techniques for Defect Prediction Models , 2020, IEEE Transactions on Software Engineering.
[3] Xiaofang Zhang,et al. Software Defect Prediction Based on Gated Hierarchical LSTMs , 2021, IEEE Transactions on Reliability.
[4] Junhua Chen,et al. International carbon financial market prediction using particle swarm optimization and support vector machine , 2021, Journal of Ambient Intelligence and Humanized Computing.
[5] Haiyan Zhou,et al. Regression analysis of intelligent education based on linear mixed effect model , 2021 .
[6] L. Prabaharan,et al. An improved convolutional neural network for abnormality detection and segmentation from human sperm images , 2021, Journal of Ambient Intelligence and Humanized Computing.
[7] Arabinda Das,et al. Prediction of Unknown Fault of Induction Motor using SVM following Decision-Directed Acyclic Graph , 2021, Journal of The Institution of Engineers (India): Series B.
[8] H. Kalluri,et al. Image classification using regularized convolutional neural network design with dimensionality reduction modules: RCNN–DRM , 2021, J. Ambient Intell. Humaniz. Comput..
[9] Sang-Bong Rhee,et al. A practical solution based on convolutional neural network for non-intrusive load monitoring , 2021, Journal of Ambient Intelligence and Humanized Computing.
[10] Cong Jin,et al. Cross-project software defect prediction based on domain adaptation learning and optimization , 2021, Expert Syst. Appl..
[11] Fatih Yücalar,et al. Multiple-classifiers in software quality engineering: Combining predictors to improve software fault prediction ability , 2020 .
[12] Bin Liu,et al. Software defect prediction based on correlation weighted class association rule mining , 2020, Knowl. Based Syst..
[13] Heli Sun,et al. Collaborative filtering based recommendation of sampling methods for software defect prediction , 2020, Appl. Soft Comput..
[14] Pijush Samui,et al. Forecasting heating and cooling loads of buildings: a comparative performance analysis , 2020, J. Ambient Intell. Humaniz. Comput..
[15] Ning Li,et al. A Systematic Review of Unsupervised Learning Techniques for Software Defect Prediction , 2019, Inf. Softw. Technol..
[16] Zhou Xu,et al. Cross Project Defect Prediction via Balanced Distribution Adaptation Based Transfer Learning , 2019, Journal of Computer Science and Technology.
[17] Qing Gu,et al. DP-Share: Privacy-Preserving Software Defect Prediction Model Sharing Through Differential Privacy , 2019, Journal of Computer Science and Technology.
[18] Wei Wang,et al. Effective android malware detection with a hybrid model based on deep autoencoder and convolutional neural network , 2018, Journal of Ambient Intelligence and Humanized Computing.
[19] Katerina Goseva-Popstojanova,et al. Software Fault Proneness Prediction with Group Lasso Regression: On Factors that Affect Classification Performance , 2019, 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC).
[20] Tao Zhang,et al. Software defect prediction based on kernel PCA and weighted extreme learning machine , 2019, Inf. Softw. Technol..
[21] Xiang Chen,et al. Software defect number prediction: Unsupervised vs supervised methods , 2019, Inf. Softw. Technol..
[22] Chiranjib Sur,et al. DeepSeq: learning browsing log data based personalized security vulnerabilities and counter intelligent measures , 2018, J. Ambient Intell. Humaniz. Comput..
[23] C. Shoba Bindu,et al. Class level software fault prediction using step wise linear regression , 2018, International Journal of Engineering & Technology.
[24] Wushao Wen,et al. Ridge and Lasso Regression Models for Cross-Version Defect Prediction , 2018, IEEE Transactions on Reliability.
[25] Xiao-Yuan Jing,et al. Progress on approaches to software defect prediction , 2018, IET Softw..
[26] Aditya K. Ghose,et al. A deep tree-based model for software defect prediction , 2018, ArXiv.
[27] Sushant Kumar Pandey,et al. Software Bug Prediction Prototype Using Bayesian Network Classifier: A Comprehensive Model , 2018 .
[28] Sandeep Kumar,et al. A study on software fault prediction techniques , 2019, Artificial Intelligence Review.
[29] S. Chatterjee,et al. A bayesian belief network based model for predicting software faults in early phase of software development process , 2018, Applied Intelligence.
[30] Daoxu Chen,et al. A Cluster Based Feature Selection Method for Cross-Project Software Defect Prediction , 2017, Journal of Computer Science and Technology.
[31] Sandeep Kumar,et al. Towards an ensemble based system for predicting the number of software faults , 2017, Expert Syst. Appl..
[32] Amjad Hudaib,et al. Software Defect Prediction using Feature Selection and Random Forest Algorithm , 2017, 2017 International Conference on New Trends in Computing Sciences (ICTCS).
[33] S. Rathore,et al. A study on software fault prediction techniques , 2017, Artificial Intelligence Review.
[34] G. Brassington,et al. Mean absolute error and root mean square error: which is the better metric for assessing model performance? , 2017 .
[35] Masoud Shafiee,et al. A New Kalman Filter Based 2D AR Model Parameter Estimation Method , 2017 .
[36] Xin Xia,et al. High-Impact Bug Report Identification with Imbalanced Learning Strategies , 2017, Journal of Computer Science and Technology.
[37] Pankaj Kumar,et al. Defect Prediction Model for AOP-based Software Development using Hybrid Fuzzy C-Means with Genetic Algorithm and K-Nearest Neighbors Classifier , 2016 .
[38] F. Valles-Barajas. A comparative analysis between two techniques for the prediction of software defects: fuzzy and statistical linear regression , 2015, Innovations in Systems and Software Engineering.
[39] Jongmoon Baik,et al. A Hybrid Instance Selection Using Nearest-Neighbor for Cross-Project Defect Prediction , 2015, Journal of Computer Science and Technology.
[40] Ebru Akcapinar Sezer,et al. A comparison of some soft computing methods for software fault prediction , 2015, Expert Syst. Appl..
[41] Xin Yao,et al. A Learning-to-Rank Approach to Software Defect Prediction , 2015, IEEE Transactions on Reliability.
[42] Ruchika Malhotra,et al. A systematic review of machine learning techniques for software fault prediction , 2015, Appl. Soft Comput..
[43] Ali Selamat,et al. Important issues in software fault prediction: A road map , 2014 .
[44] Ćemal B. Dolićanin,et al. The Geometric Approach , 2014 .
[45] Tian Jiang,et al. Personalized defect prediction , 2013, 2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE).
[46] Richard Torkar,et al. Software fault prediction metrics: A systematic literature review , 2013, Inf. Softw. Technol..
[47] James W. Hardin,et al. Exact Wilcoxon Signed-Rank and Wilcoxon Mann–Whitney Ranksum Tests , 2013 .
[48] David B. Dunson,et al. Bayesian monotone regression using Gaussian process projection , 2013, 1306.4041.
[49] Bart Baesens,et al. Toward Comprehensible Software Fault Prediction Models Using Bayesian Network Classifiers , 2013, IEEE Transactions on Software Engineering.
[50] Dimi Kyriakou,et al. A practical solution , 2013 .
[51] P. Bromiley. Products and Convolutions of Gaussian Probability Density Functions , 2013 .
[52] Olcay Taner Yildiz,et al. Software defect prediction using Bayesian networks , 2012, Empirical Software Engineering.
[53] Lahouari Ghouti,et al. Efficient prediction of software fault proneness modules using support vector machines and probabilistic neural networks , 2011, 2011 Malaysian Conference in Software Engineering.
[54] Vikramaditya R. Jakkula,et al. Tutorial on Support Vector Machine ( SVM ) , 2011 .
[55] Lech Madeyski,et al. Towards identifying software project clusters with regard to defect prediction , 2010, PROMISE '10.
[56] Ping Guo,et al. Software Defect Prediction Using Fuzzy Support Vector Regression , 2010, ISNN.
[57] Elaine J. Weyuker,et al. Comparing the effectiveness of several modeling methods for fault prediction , 2010, Empirical Software Engineering.
[58] Koichiro Ochimizu,et al. Towards logistic regression models for predicting fault-prone code across software projects , 2009, 2009 3rd International Symposium on Empirical Software Engineering and Measurement.
[59] Arvinder Kaur,et al. Software Fault Proneness Prediction Using Support Vector Machines , 2009 .
[60] Yue Jiang,et al. Techniques for evaluating fault prediction models , 2008, Empirical Software Engineering.
[61] Wang Qing,et al. Software Defect Prediction , 2008 .
[62] Joanne Bechta Dugan,et al. Empirical Analysis of Software Fault Content and Fault Proneness Using Bayesian Methods , 2007, IEEE Transactions on Software Engineering.
[63] Andy Liaw,et al. Classification and Regression by randomForest , 2007 .
[64] William Marsh,et al. Predicting software defects in varying development lifecycles using Bayesian nets , 2007, Inf. Softw. Technol..
[65] Sergios Theodoridis,et al. A geometric approach to Support Vector Machine (SVM) classification , 2006, IEEE Transactions on Neural Networks.
[66] Qinbao Song,et al. Software defect association mining and defect correction effort prediction , 2006, IEEE Transactions on Software Engineering.
[67] Wei Chu,et al. Gaussian Processes for Ordinal Regression , 2005, J. Mach. Learn. Res..
[68] Malik Beshir Malik,et al. Applied Linear Regression , 2005, Technometrics.
[69] Nando de Freitas,et al. An Introduction to MCMC for Machine Learning , 2004, Machine Learning.
[70] Robert P. Sheridan,et al. Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling , 2003, J. Chem. Inf. Comput. Sci..
[71] R. Manthalkar,et al. A Survey of Rotation Invariant Texture Classification Methods , 2002 .
[72] Petros Dellaportas,et al. On Bayesian model and variable selection using MCMC , 2002, Stat. Comput..
[73] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..
[74] Martin J. Shepperd,et al. Comparing Software Prediction Techniques Using Simulation , 2001, IEEE Trans. Software Eng..
[75] Barry W. Boehm,et al. Software Defect Reduction Top 10 List , 2001, Computer.
[76] Henry W. Altland,et al. Regression Analysis: Statistical Modeling of a Response Variable , 1998, Technometrics.
[77] Shinichi Morishita,et al. On Classification and Regression , 1998, Discovery Science.
[78] Taghi M. Khoshgoftaar,et al. Predicting fault-prone modules with case-based reasoning , 1997, Proceedings The Eighth International Symposium on Software Reliability Engineering.
[79] S. Wold,et al. The Collinearity Problem in Linear Regression. The Partial Least Squares (PLS) Approach to Generalized Inverses , 1984 .