Empirical Study of Software Defect Prediction: A Systematic Mapping
暂无分享,去创建一个
Manju Khari | Le Hoang Son | Raghvendra Kumar | Pham Huy Thong | Nakul Pritam | Pham Thi Minh Phuong | Manju Khari | Raghvendra Kumar | Nakul Pritam
[1] Edward B. Allen,et al. GP-based software quality prediction , 1998 .
[2] A. En-Nouaary,et al. Catalog of Metrics for Assessing Security Risks of Software throughout the Software Development Life Cycle , 2008, 2008 International Conference on Information Security and Assurance (isa 2008).
[3] Letha H. Etzkorn,et al. Empirical Validation of Three Software Metrics Suites to Predict Fault-Proneness of Object-Oriented Classes Developed Using Highly Iterative or Agile Software Development Processes , 2007, IEEE Transactions on Software Engineering.
[4] Raed Shatnawi,et al. Finding software metrics threshold values using ROC curves , 2010, J. Softw. Maintenance Res. Pract..
[5] Elaine J. Weyuker,et al. Comparing the effectiveness of several modeling methods for fault prediction , 2010, Empirical Software Engineering.
[6] Bart Baesens,et al. Toward Comprehensible Software Fault Prediction Models Using Bayesian Network Classifiers , 2013, IEEE Transactions on Software Engineering.
[7] Tim Menzies,et al. Data Mining Static Code Attributes to Learn Defect Predictors , 2007, IEEE Transactions on Software Engineering.
[8] Nilanjan Dey,et al. Dental diagnosis from X-Ray images: An expert system based on fuzzy computing , 2018, Biomed. Signal Process. Control..
[9] Ayse Basar Bener,et al. Software Defect Prediction Using Call Graph Based Ranking (CGBR) Framework , 2008, 2008 34th Euromicro Conference Software Engineering and Advanced Applications.
[10] Lionel C. Briand,et al. Exploring the relationships between design measures and software quality in object-oriented systems , 2000, J. Syst. Softw..
[11] Lionel C. Briand,et al. A systematic and comprehensive investigation of methods to build and evaluate fault prediction models , 2010, J. Syst. Softw..
[12] R. Geetha Ramani,et al. Predicting fault-prone software modules using feature selection and classification through data mining algorithms , 2012, 2012 IEEE International Conference on Computational Intelligence and Computing Research.
[13] Bart Baesens,et al. Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings , 2008, IEEE Transactions on Software Engineering.
[14] Akito Monden,et al. Comparison of Outlier Detection Methods in Fault-proneness Models , 2007, ESEM 2007.
[15] Premkumar T. Devanbu,et al. An Investigation into Coupling Measures for C++ , 1997, Proceedings of the (19th) International Conference on Software Engineering.
[16] Guangchun Luo,et al. Transfer learning for cross-company software defect prediction , 2012, Inf. Softw. Technol..
[17] James M. Hogan,et al. Predicting Fault-Prone Software Modules with Rank Sum Classification , 2013, 2013 22nd Australian Software Engineering Conference.
[18] Hausi A. Müller,et al. Predicting fault-proneness using OO metrics. An industrial case study , 2002, Proceedings of the Sixth European Conference on Software Maintenance and Reengineering.
[19] Banu Diri,et al. A systematic review of software fault prediction studies , 2009, Expert Syst. Appl..
[20] Venkata U. B. Challagulla,et al. A Unified Framework for Defect Data Analysis Using the MBR Technique , 2006, 2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06).
[21] W. Pedrycz,et al. Software quality prediction using median-adjusted class labels , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).
[22] ZhangHongyu,et al. Comments on "Data Mining Static Code Attributes to Learn Defect Predictors" , 2007 .
[23] A. J. Paul,et al. Modelling the investment casting process: a novel approach for view factor calculations and defect prediction , 1995 .
[24] Burak Turhan,et al. Implications of ceiling effects in defect predictors , 2008, PROMISE '08.
[25] Parag C. Pendharkar,et al. Exhaustive and heuristic search approaches for learning a software defect prediction model , 2010, Eng. Appl. Artif. Intell..
[26] Raed Shatnawi,et al. An empirical study of the bad smells and class error probability in the post-release object-oriented system evolution , 2007, J. Syst. Softw..
[27] Zhan Li,et al. A practical method for the software fault-prediction , 2007, 2007 IEEE International Conference on Information Reuse and Integration.
[28] Ning Chen,et al. Software process evaluation: a machine learning framework with application to defect management process , 2013, Empirical Software Engineering.
[29] Taghi M. Khoshgoftaar,et al. Predicting Faults in High Assurance Software , 2010, 2010 IEEE 12th International Symposium on High Assurance Systems Engineering.
[30] Banu Diri,et al. An Artificial Immune System Approach for Fault Prediction in Object-Oriented Software , 2007, 2nd International Conference on Dependability of Computer Systems (DepCoS-RELCOMEX '07).
[31] Yourong Li,et al. Short-term fault prediction based on support vector machines with parameter optimization by evolution strategy , 2009, Expert Syst. Appl..
[32] Raed Shatnawi,et al. The effectiveness of software metrics in identifying error-prone classes in post-release software evolution process , 2008, J. Syst. Softw..
[33] Arie van Deursen,et al. A Model of Maintainability - Suggestion for Future Research , 2006, Software Engineering Research and Practice.
[34] Bharavi Mishra,et al. Impact of attribute selection on defect proneness prediction in OO software , 2011, 2011 2nd International Conference on Computer and Communication Technology (ICCCT-2011).
[35] Xiang Chen,et al. MULTI: Multi-objective effort-aware just-in-time software defect prediction , 2018, Inf. Softw. Technol..
[36] A. Zeller,et al. Predicting Defects for Eclipse , 2007, Third International Workshop on Predictor Models in Software Engineering (PROMISE'07: ICSE Workshops 2007).
[37] Haibo He,et al. Learning from Imbalanced Data , 2009, IEEE Transactions on Knowledge and Data Engineering.
[38] Koichiro Ochimizu,et al. Towards logistic regression models for predicting fault-prone code across software projects , 2009, ESEM 2009.
[39] Michele Lanza,et al. An extensive comparison of bug prediction approaches , 2010, 2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010).
[40] Yong Hu,et al. Systematic literature review of machine learning based software development effort estimation models , 2012, Inf. Softw. Technol..
[41] Bart Baesens,et al. Mining software repositories for comprehensible software fault prediction models , 2008, J. Syst. Softw..
[42] Tracy Hall,et al. Developing Fault-Prediction Models: What the Research Can Show Industry , 2011, IEEE Software.
[43] Aurora Trinidad Ramirez Pozo,et al. A symbolic fault-prediction model based on multiobjective particle swarm optimization , 2010, J. Syst. Softw..
[44] Tihana Galinac Grbac,et al. Co-evolutionary multi-population genetic programming for classification in software defect prediction: An empirical case study , 2017, Appl. Soft Comput..
[45] Jung-Hua Lo,et al. Predicting Software Reliability with Support Vector Machines , 2010, 2010 Second International Conference on Computer Research and Development.
[46] Xiaofei Wang,et al. Artificial Intelligence-Based Techniques for Emerging Heterogeneous Network: State of the Arts, Opportunities, and Challenges , 2015, IEEE Access.
[47] Bhavani M. Thuraisingham,et al. Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints , 2011, IEEE Transactions on Knowledge and Data Engineering.
[48] Banu Diri,et al. Practical development of an Eclipse-based software fault prediction tool using Naive Bayes algorithm , 2011, Expert Syst. Appl..
[49] Taghi M. Khoshgoftaar,et al. Evolutionary Optimization of Software Quality Modeling with Multiple Repositories , 2010, IEEE Transactions on Software Engineering.
[50] Olcay Taner Yildiz,et al. Software defect prediction using Bayesian networks , 2012, Empirical Software Engineering.
[51] Pradeep Singh,et al. An Investigation of the Effect of Discretization on Defect Prediction Using Static Measures , 2009, 2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies.
[52] Le Hoang Son,et al. Bipolar neutrosophic soft sets and applications in decision making , 2017, J. Intell. Fuzzy Syst..
[53] Hong-Zhong Huang,et al. Early Software Quality Prediction Based on a Fuzzy Neural Network Model , 2007, Third International Conference on Natural Computation (ICNC 2007).
[54] Hongfang Liu,et al. Building effective defect-prediction models in practice , 2005, IEEE Software.
[55] Lionel C. Briand,et al. A Unified Framework for Coupling Measurement in Object-Oriented Systems , 1999, IEEE Trans. Software Eng..
[56] Silvio Romero de Lemos Meira,et al. A Constructive RBF Neural Network for Estimating the Probability of Defects in Software Modules , 2007, 2007 International Joint Conference on Neural Networks.
[57] Tom Fawcett,et al. An introduction to ROC analysis , 2006, Pattern Recognit. Lett..
[58] Stephen G. MacDonell,et al. A comparison of techniques for developing predictive models of software metrics , 1997, Inf. Softw. Technol..
[59] Rachel Harrison,et al. A study of subgroup discovery approaches for defect prediction , 2013, Inf. Softw. Technol..
[60] Wasif Afzal,et al. Using Faults-Slip-Through Metric as a Predictor of Fault-Proneness , 2010, 2010 Asia Pacific Software Engineering Conference.
[61] Yue Jiang,et al. Techniques for evaluating fault prediction models , 2008, Empirical Software Engineering.
[62] Venkata U. B. Challagulla,et al. Empirical assessment of machine learning based software defect prediction techniques , 2005, 10th IEEE International Workshop on Object-Oriented Real-Time Dependable Systems.
[63] Qinbao Song,et al. A General Software Defect-Proneness Prediction Framework , 2011, IEEE Transactions on Software Engineering.
[64] Yuming Zhou,et al. On the ability of complexity metrics to predict fault-prone classes in object-oriented systems , 2010, J. Syst. Softw..
[65] Michelle Cartwright,et al. An Empirical Investigation of an Object-Oriented Software System , 2000, IEEE Trans. Software Eng..
[66] Javam C. Machado,et al. The prediction of faulty classes using object-oriented design metrics , 2001, J. Syst. Softw..
[67] Vandana Bhattacherjee,et al. Software Fault Prediction Using Quad Tree-Based K-Means Clustering Algorithm , 2012, IEEE Transactions on Knowledge and Data Engineering.
[68] Yann-Gaël Guéhéneuc,et al. Design evolution metrics for defect prediction in object oriented systems , 2010, Empirical Software Engineering.
[69] Abraham Bernstein,et al. Predicting defect densities in source code files with decision tree learners , 2006, MSR '06.
[70] Tao Huang,et al. IEEE Access Special Section Editorial: Recent Advances in Software Defined Networking for 5G Networks , 2015, IEEE Access.
[71] Ye Yang,et al. An investigation on the feasibility of cross-project defect prediction , 2012, Automated Software Engineering.
[72] Valery Buzungu,et al. Predicting Fault-prone Components in a Java Legacy System , 2006 .
[73] Mark Harman,et al. A Theoretical and Empirical Study of Search-Based Testing: Local, Global, and Hybrid Search , 2010, IEEE Transactions on Software Engineering.
[74] Taghi M. Khoshgoftaar,et al. Feature Selection with Imbalanced Data for Software Defect Prediction , 2009, 2009 International Conference on Machine Learning and Applications.
[75] Tong-Seng Quah,et al. Prediction of software development faults in PL/SQL files using neural network models , 2004, Inf. Softw. Technol..
[76] Arashdeep Kaur,et al. An empirical approach for software fault prediction , 2010, 2010 5th International Conference on Industrial and Information Systems.
[77] Ken-ichi Matsumoto,et al. Locating Source Code to Be Fixed Based on Initial Bug Reports - A Case Study on the Eclipse Project , 2012, 2012 Fourth International Workshop on Empirical Software Engineering in Practice.
[78] Ayse Basar Bener,et al. Software Defect Prediction: Heuristics for Weighted Naïve Bayes , 2007, ICSOFT.
[79] S. Kanmani,et al. Object-oriented software fault prediction using neural networks , 2007, Inf. Softw. Technol..
[80] Xiuzhen Zhang,et al. Comments on "Data Mining Static Code Attributes to Learn Defect Predictors" , 2007, IEEE Trans. Software Eng..
[81] N. Madhavji,et al. Validating Object-Oriented Design Metrics on a Commercial Java Application , 2000 .
[82] Le Hoang Son,et al. Picture inference system: a new fuzzy inference system on picture fuzzy set , 2016, Applied Intelligence.
[83] Le Hoang Son,et al. A Neutrosophic Recommender System for Medical Diagnosis Based on Algebraic Neutrosophic Measures , 2016, Appl. Soft Comput..
[84] Ayse Basar Bener,et al. An industrial case study of classifier ensembles for locating software defects , 2011, Software Quality Journal.
[85] Yue Jiang,et al. Fault Prediction using Early Lifecycle Data , 2007, The 18th IEEE International Symposium on Software Reliability (ISSRE '07).
[86] Tracy Hall,et al. The State of Machine Learning Methodology in Software Fault Prediction , 2012, 2012 11th International Conference on Machine Learning and Applications.
[87] Ayse Basar Bener,et al. On the relative value of cross-company and within-company data for defect prediction , 2009, Empirical Software Engineering.
[88] Jan Chudzikiewicz,et al. Software Metrics for Similarity Determination of Complex Software Systems , 2018, KKIO Software Engineering Conference.
[89] Rudolf Ferenc,et al. Using the Conceptual Cohesion of Classes for Fault Prediction in Object-Oriented Systems , 2008, IEEE Transactions on Software Engineering.
[90] A. Gupta,et al. Investigating object-oriented design metrics to predict fault-proneness of software modules , 2012, 2012 CSI Sixth International Conference on Software Engineering (CONSEG).
[91] Manju Khari,et al. COMPARISON OF SIX PRIORITIZATION TECHNIQUES FOR SOFTWARE REQUIREMENTS , 2013 .
[92] Joanne Bechta Dugan,et al. Empirical Analysis of Software Fault Content and Fault Proneness Using Bayesian Methods , 2007, IEEE Transactions on Software Engineering.
[93] Yue Jiang,et al. Misclassification cost-sensitive fault prediction models , 2009, PROMISE '09.
[94] Parvinder S. Sandhu,et al. A model for early prediction of faults in software systems , 2010, 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE).
[95] Yuming Zhou,et al. Empirical Analysis of Object-Oriented Design Metrics for Predicting High and Low Severity Faults , 2006, IEEE Transactions on Software Engineering.
[96] Akito Monden,et al. Assessing the Cost Effectiveness of Fault Prediction in Acceptance Testing , 2013, IEEE Transactions on Software Engineering.
[97] K. Kaminsky,et al. Building a genetically engineerable evolvable program (GEEP) using breadth-based explicit knowledge for predicting software defects , 2004, IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04..
[98] Laurie A. Williams,et al. An empirical model to predict security vulnerabilities using code complexity metrics , 2008, ESEM '08.
[99] Manju Khari,et al. Heuristic search-based approach for automated test data generation: a survey , 2013, Int. J. Bio Inspired Comput..
[100] Iker Gondra,et al. Applying machine learning to software fault-proneness prediction , 2008, J. Syst. Softw..
[101] Taghi M. Khoshgoftaar,et al. An empirical study of predicting software faults with case-based reasoning , 2006, Software Quality Journal.
[102] Ahmed E. Hassan,et al. Think locally, act globally: Improving defect and effort prediction models , 2012, 2012 9th IEEE Working Conference on Mining Software Repositories (MSR).
[103] P. Singh,et al. Empirical investigation of fault prediction capability of object oriented metrics of open source software , 2012, 2012 Ninth International Conference on Computer Science and Software Engineering (JCSSE).
[104] Danielle Azar,et al. An ant colony optimization algorithm to improve software quality prediction models: Case of class stability , 2011, Inf. Softw. Technol..
[105] Lionel C. Briand,et al. Data Mining Techniques for Building Fault-proneness Models in Telecom Java Software , 2007, The 18th IEEE International Symposium on Software Reliability (ISSRE '07).
[106] G. Denaro,et al. An empirical evaluation of fault-proneness models , 2002, Proceedings of the 24th International Conference on Software Engineering. ICSE 2002.
[107] Wasif Afzal,et al. On the application of genetic programming for software engineering predictive modeling: A systematic review , 2011, Expert Syst. Appl..
[108] Jehad Al Dallal. The impact of accounting for special methods in the measurement of object-oriented class cohesion on refactoring and fault prediction activities , 2012, J. Syst. Softw..
[109] Taghi M. Khoshgoftaar,et al. Tree-based software quality estimation models for fault prediction , 2002, Proceedings Eighth IEEE Symposium on Software Metrics.
[110] Tim Menzies. The Unreasonable Effectiveness of Software Analytics , 2018, IEEE Software.
[111] Shinji Kusumoto,et al. Prediction of fault-proneness at early phase in object-oriented development , 1999, Proceedings 2nd IEEE International Symposium on Object-Oriented Real-Time Distributed Computing (ISORC'99) (Cat. No.99-61702).
[112] RadjenovićDanijel,et al. Software fault prediction metrics , 2013 .
[113] Ayse Basar Bener,et al. Analysis of Naive Bayes' assumptions on software fault data: An empirical study , 2009, Data Knowl. Eng..
[114] Raed Shatnawi,et al. A Quantitative Investigation of the Acceptable Risk Levels of Object-Oriented Metrics in Open-Source Systems , 2010, IEEE Transactions on Software Engineering.
[115] Wasif Afzal,et al. A Comparative Evaluation of Using Genetic Programming for Predicting Fault Count Data , 2008, 2008 The Third International Conference on Software Engineering Advances.
[116] Burak Turhan,et al. Mining Software Data , 2007, 2007 IEEE 23rd International Conference on Data Engineering Workshop.
[117] Laurie A. Williams,et al. Evaluating Complexity, Code Churn, and Developer Activity Metrics as Indicators of Software Vulnerabilities , 2011, IEEE Transactions on Software Engineering.
[118] Bo Yu,et al. Extract rules from software quality prediction model based on neural network , 2004, 16th IEEE International Conference on Tools with Artificial Intelligence.
[119] Peter Kokol,et al. Estimating Software Quality with Advanced Data Mining Techniques , 2006, 2006 International Conference on Software Engineering Advances (ICSEA'06).
[120] Ayse Basar Bener,et al. Defect prediction from static code features: current results, limitations, new approaches , 2010, Automated Software Engineering.
[121] Banu Diri,et al. Investigating the effect of dataset size, metrics sets, and feature selection techniques on software fault prediction problem , 2009, Inf. Sci..
[122] Manju Khari,et al. Web Services Vulnerability Testing Using Open source Security Scanners : An experimental Study , 2014 .
[123] Bojan Cukic,et al. Predicting fault prone modules by the Dempster-Shafer belief networks , 2003, 18th IEEE International Conference on Automated Software Engineering, 2003. Proceedings..
[124] Prabhat Kumar,et al. A Novel Approach for Software Test Data Generation using Cuckoo Algorithm , 2016, ICTCS.
[125] Lars Lundberg,et al. Statistical models vs. expert estimation for fault prediction in modified code - an industrial case study , 2007, J. Syst. Softw..
[126] Bhekisipho Twala,et al. Software faults prediction using multiple classifiers , 2011, 2011 3rd International Conference on Computer Research and Development.
[127] Gerardo Canfora,et al. Impact analysis by mining software and change request repositories , 2005, 11th IEEE International Software Metrics Symposium (METRICS'05).
[128] Manju Khari,et al. Neutrosophic soft set decision making for stock trending analysis , 2018, Evol. Syst..
[129] Ebru Akcapinar Sezer,et al. A comparison of some soft computing methods for software fault prediction , 2015, Expert Syst. Appl..
[130] Le Hoang Son,et al. Tune Up Fuzzy C-Means for Big Data: Some Novel Hybrid Clustering Algorithms Based on Initial Selection and Incremental Clustering , 2017, Int. J. Fuzzy Syst..
[131] Victor R. Basili,et al. A Validation of Object-Oriented Design Metrics as Quality Indicators , 1996, IEEE Trans. Software Eng..
[132] Tim Menzies,et al. Better cross company defect prediction , 2013, 2013 10th Working Conference on Mining Software Repositories (MSR).
[133] Xiong Luo,et al. Web Service QoS Prediction Based on Adaptive Dynamic Programming Using Fuzzy Neural Networks for Cloud Services , 2015, IEEE Access.
[134] Taghi M. Khoshgoftaar,et al. Software quality estimation with limited fault data: a semi-supervised learning perspective , 2007, Software Quality Journal.
[135] Ayse Basar Bener,et al. Validation of network measures as indicators of defective modules in software systems , 2009, PROMISE '09.
[136] Yi Zhang,et al. Classifying Software Changes: Clean or Buggy? , 2008, IEEE Transactions on Software Engineering.
[137] Nguyen Thanh Tung,et al. Segmentation of dental X-ray images in medical imaging using neutrosophic orthogonal matrices , 2018, Expert Syst. Appl..
[138] Taghi M. Khoshgoftaar,et al. Fault Prediction Modeling for Software Quality Estimation: Comparing Commonly Used Techniques , 2003, Empirical Software Engineering.
[139] Haruhiko Kaiya,et al. Adapting a fault prediction model to allow inter languagereuse , 2008, PROMISE '08.
[140] Michele Lanza,et al. Evaluating defect prediction approaches: a benchmark and an extensive comparison , 2011, Empirical Software Engineering.
[141] Wasif Afzal,et al. Search-based Prediction of Fault-slip-through in Large Software Projects , 2010, 2nd International Symposium on Search Based Software Engineering.
[142] Ye Yang,et al. Predicting Fault-Prone Modules: A Comparative Study , 2009, SEAFOOD.
[143] Bojan Cukic,et al. Robust prediction of fault-proneness by random forests , 2004, 15th International Symposium on Software Reliability Engineering.
[144] Manju Khari,et al. Analysis on Intrusion Detection by Machine Learning Techniques: A Review , 2013 .
[145] Barbara Paech,et al. Exploring the relationship of a file's history and its fault-proneness: An empirical method and its application to open source programs , 2010, Inf. Softw. Technol..
[146] Khaled El Emam,et al. The Confounding Effect of Class Size on the Validity of Object-Oriented Metrics , 2001, IEEE Trans. Software Eng..
[147] Jesús S. Aguilar-Ruiz,et al. Searching for rules to detect defective modules: A subgroup discovery approach , 2012, Inf. Sci..
[148] Le Hoang Son,et al. THEORETICAL ANALYSIS OF PICTURE FUZZY CLUSTERING: CONVERGENCE AND PROPERTY , 2018, Journal of Computer Science and Cybernetics.
[149] M.J. Khan,et al. Software quality prediction techniques: A comparative analysis , 2008, 2008 4th International Conference on Emerging Technologies.
[150] Florentin Smarandache,et al. Interval Complex Neutrosophic Set: Formulation and Applications in Decision-Making , 2018, Int. J. Fuzzy Syst..
[151] Ioannis Stamelos,et al. Regression via Classification applied on software defect estimation , 2008, Expert Syst. Appl..
[152] Francisco Chiclana,et al. Dynamic structural neural network , 2018, J. Intell. Fuzzy Syst..
[153] Ming Zhao,et al. A comparison between software design and code metrics for the prediction of software fault content , 1998, Inf. Softw. Technol..
[154] Nasser M. Nasrabadi,et al. Coupled Auto-Associative Neural Networks for Heterogeneous Face Recognition , 2015, IEEE Access.
[155] Richard C. Holt,et al. The top ten list: dynamic fault prediction , 2005, 21st IEEE International Conference on Software Maintenance (ICSM'05).
[156] Wasif Afzal,et al. Search-Based Prediction of Fault Count Data , 2009, 2009 1st International Symposium on Search Based Software Engineering.
[157] Xiao-Yuan Jing,et al. Label propagation based semi-supervised learning for software defect prediction , 2016, Automated Software Engineering.
[158] Andreas Zeller,et al. Change Bursts as Defect Predictors , 2010, 2010 IEEE 21st International Symposium on Software Reliability Engineering.
[159] YuLean. An evolutionary programming based asymmetric weighted least squares support vector machine ensemble learning methodology for software repository mining , 2012 .
[160] Mumtaz Ali,et al. δ-equality of intuitionistic fuzzy sets: a new proximity measure and applications in medical diagnosis , 2018, Applied Intelligence.
[161] Tibor Gyimóthy,et al. Empirical validation of object-oriented metrics on open source software for fault prediction , 2005, IEEE Transactions on Software Engineering.
[162] Richard Torkar,et al. Software fault prediction metrics: A systematic literature review , 2013, Inf. Softw. Technol..
[163] Mumtaz Ali,et al. A Novel Clustering Algorithm in a Neutrosophic Recommender System for Medical Diagnosis , 2017, Cognitive Computation.
[164] Banu Diri,et al. Clustering and Metrics Thresholds Based Software Fault Prediction of Unlabeled Program Modules , 2009, 2009 Sixth International Conference on Information Technology: New Generations.
[165] Mahmoud O. Elish,et al. Empirical comparison of three metrics suites for fault prediction in packages of object-oriented systems: A case study of Eclipse , 2011, Adv. Eng. Softw..
[166] Nilanjan Dey,et al. A survey of the state-of-the-arts on neutrosophic sets in biomedical diagnoses , 2019, Int. J. Mach. Learn. Cybern..
[167] Filomena Ferrucci,et al. A Genetic Algorithm to Configure Support Vector Machines for Predicting Fault-Prone Components , 2011, PROFES.
[168] Elaine J. Weyuker,et al. Predicting the location and number of faults in large software systems , 2005, IEEE Transactions on Software Engineering.
[169] Arashdeep Kaur,et al. Early Software Fault Prediction Using Real Time Defect Data , 2009, 2009 Second International Conference on Machine Vision.
[170] Mik Kersten. What Flows through a Software Value Stream? , 2018, IEEE Software.
[171] Aurora Trinidad Ramirez Pozo,et al. Predicting Fault Proneness of Classes Trough a Multiobjective Particle Swarm Optimization Algorithm , 2008, 2008 20th IEEE International Conference on Tools with Artificial Intelligence.
[172] Witold Pedrycz,et al. A comparative analysis of the efficiency of change metrics and static code attributes for defect prediction , 2008, 2008 ACM/IEEE 30th International Conference on Software Engineering.
[173] Taghi M. Khoshgoftaar,et al. An application of fuzzy clustering to software quality prediction , 2000, Proceedings 3rd IEEE Symposium on Application-Specific Systems and Software Engineering Technology.
[174] Mumtaz Ali,et al. H-max distance measure of intuitionistic fuzzy sets in decision making , 2018, Appl. Soft Comput..
[175] Antonio García Cabot,et al. Performing systematic literature review in software engineering , 2012 .
[176] Scott Dick,et al. Evaluating Stratification Alternatives to Improve Software Defect Prediction , 2012, IEEE Transactions on Reliability.
[177] Tim Menzies,et al. Actionable Analytics for Software Engineering , 2018, IEEE Softw..
[178] Nan-Hsing Chiu,et al. Combining techniques for software quality classification: An integrated decision network approach , 2011, Expert Syst. Appl..
[179] Ekkehard Baisch,et al. Comparison of conventional approaches and soft-computing approaches for software quality prediction , 1999 .
[180] Michael R. Lyu,et al. A novel method for early software quality prediction based on support vector machine , 2005, 16th IEEE International Symposium on Software Reliability Engineering (ISSRE'05).
[181] Manoj Kumar,et al. Analysis of software security testing using metaheuristic search technique , 2016, 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom).
[182] Karim O. Elish,et al. Predicting defect-prone software modules using support vector machines , 2008, J. Syst. Softw..
[183] Lean Yu,et al. An evolutionary programming based asymmetric weighted least squares support vector machine ensemble learning methodology for software repository mining , 2012, Inf. Sci..
[184] Jun Zheng,et al. Cost-sensitive boosting neural networks for software defect prediction , 2010, Expert Syst. Appl..
[185] Tracy Hall,et al. A Systematic Literature Review on Fault Prediction Performance in Software Engineering , 2012, IEEE Transactions on Software Engineering.