Linear and non-linear bayesian regression methods for software fault prediction

[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 .