On the application of search-based techniques for software engineering predictive modeling: A systematic review and future directions
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[1] Cong Jin,et al. Prediction approach of software fault-proneness based on hybrid artificial neural network and quantum particle swarm optimization , 2015, Appl. Soft Comput..
[2] Ömer Faruk Arar,et al. Software defect prediction using cost-sensitive neural network , 2015, Appl. Soft Comput..
[3] José Javier Dolado,et al. On the problem of the software cost function , 2001, Inf. Softw. Technol..
[4] Ricardo Massa Ferreira Lima,et al. GA-based method for feature selection and parameters optimization for machine learning regression applied to software effort estimation , 2010, Inf. Softw. Technol..
[5] Yigang Wang,et al. A new software maintainability evaluation model based on multiple classifiers combination , 2013, 2013 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE).
[6] Daryl Essam,et al. Software project effort estimation using genetic programming , 2002, IEEE 2002 International Conference on Communications, Circuits and Systems and West Sino Expositions.
[7] Q. Lu,et al. Software defect prediction using fuzzy integral fusion based on GA-FM , 2014, Wuhan University Journal of Natural Sciences.
[8] Yuanyuan Zhang,et al. Achievements, Open Problems and Challenges for Search Based Software Testing , 2015, 2015 IEEE 8th International Conference on Software Testing, Verification and Validation (ICST).
[9] Tim Menzies,et al. Data Mining Static Code Attributes to Learn Defect Predictors , 2007, IEEE Transactions on Software Engineering.
[10] Ruchika Malhotra,et al. A new metric for predicting software change using gene expression programming , 2014, WETSoM 2014.
[11] Taghi M. Khoshgoftaar,et al. Genetic programming model for software quality classification , 2001, Proceedings Sixth IEEE International Symposium on High Assurance Systems Engineering. Special Topic: Impact of Networking.
[12] Li Juelong,et al. A new model for software defect prediction using Particle Swarm Optimization and support vector machine , 2013, 2013 25th Chinese Control and Decision Conference (CCDC).
[13] Tim Menzies,et al. Data Mining Static Code Attributes to Learn Defect Predictors , 2007 .
[14] Siti Zaiton Mohd Hashim,et al. A flexible method to estimate the software development effort based on the classification of projects and localization of comparisons , 2013, Empirical Software Engineering.
[15] Alaa F. Sheta,et al. Estimation of the COCOMO Model Parameters Using Genetic Algorithms for NASA Software Projects , 2006 .
[16] Thong Ngee Goh,et al. A study of project selection and feature weighting for analogy based software cost estimation , 2009, J. Syst. Softw..
[17] André Carlos Ponce de Leon Ferreira de Carvalho,et al. Software effort prediction: a hyper-heuristic decision-tree based approach , 2013, SAC '13.
[18] K. Anuradha,et al. Adaptive PSO Based Association Rule Mining Technique for Software Defect Classification Using ANN , 2015 .
[19] Mark Harman,et al. The relationship between search based software engineering and predictive modeling , 2010, PROMISE '10.
[20] Tarun Kumar Rawat,et al. Bat Algorithm: Application to Adaptive Infinite Impulse Response System Identification , 2016, Arabian Journal for Science and Engineering.
[21] Silvio Romero de Lemos Meira,et al. An evolutionary morphological approach for software development cost estimation , 2012, Neural Networks.
[22] Xin Yao,et al. An analysis of multi-objective evolutionary algorithms for training ensemble models based on different performance measures in software effort estimation , 2013, PROMISE.
[23] Jesús S. Aguilar-Ruiz,et al. Searching for rules to detect defective modules: A subgroup discovery approach , 2012, Inf. Sci..
[24] Yuanyuan Zhang,et al. Search based software engineering for software product line engineering: a survey and directions for future work , 2014, SPLC.
[25] Shadi Banitaan,et al. A Better Case Adaptation Method for Case-Based Effort Estimation Using Multi-objective Optimization , 2014, 2014 13th International Conference on Machine Learning and Applications.
[26] André Carlos Ponce de Leon Ferreira de Carvalho,et al. A grammatical evolution approach for software effort estimation , 2013, GECCO '13.
[27] F. Ahmed,et al. Integrating Function Point Project Information for Improving the Accuracy of Effort Estimation , 2008, 2008 The Second International Conference on Advanced Engineering Computing and Applications in Sciences.
[28] Filomena Ferrucci,et al. Genetic Programming for Effort Estimation: An Analysis of the Impact of Different Fitness Functions , 2010, 2nd International Symposium on Search Based Software Engineering.
[29] Sheng-Yu Huang,et al. Research on Software Effort Estimation Combined with Genetic Algorithm and Support Vector Regression , 2011, 2011 International Symposium on Computer Science and Society.
[30] Alaa F. Sheta,et al. Software effort estimation by tuning COOCMO model parameters using differential evolution , 2010, ACS/IEEE International Conference on Computer Systems and Applications - AICCSA 2010.
[31] Lionel C. Briand,et al. A Systematic Review of the Application and Empirical Investigation of Search-Based Test Case Generation , 2010, IEEE Transactions on Software Engineering.
[32] Colin J Burgess,et al. Can genetic programming improve software effort estimation? A comparative evaluation , 2001, Inf. Softw. Technol..
[33] Ruchika Malhotra,et al. Application of Evolutionary Algorithms for Software Maintainability Prediction using Object-Oriented Metrics , 2014, BICT.
[34] Georgios Dounias,et al. Predicting Defects in Software Using Grammar-Guided Genetic Programming , 2008, SETN.
[35] M. B. Abdul Hamid,et al. Short Term Load Forecasting Using an Artificial Neural Network Trained by Artificial Immune System Learning Algorithm , 2010, 2010 12th International Conference on Computer Modelling and Simulation.
[36] Taghi M. Khoshgoftaar,et al. Reducing overfitting in genetic programming models for software quality classification , 2004, Eighth IEEE International Symposium on High Assurance Systems Engineering, 2004. Proceedings..
[37] Haibo He,et al. Learning from Imbalanced Data , 2009, IEEE Transactions on Knowledge and Data Engineering.
[38] Carl G. Davis,et al. A Hierarchical Model for Object-Oriented Design Quality Assessment , 2002, IEEE Trans. Software Eng..
[39] Tarun Kumar Rawat,et al. Optimal Design of 2D FIR Filters with Quadrantally Symmetric Properties Using Fractional Derivative Constraints , 2016, Circuits Syst. Signal Process..
[40] Arvinder Kaur,et al. Prediction of Software Quality Model Using Gene Expression Programming , 2009, PROFES.
[41] Martin J. Shepperd,et al. Search Heuristics, Case-based Reasoning And Software Project Effort Prediction , 2002, GECCO.
[42] Sun-Jen Huang,et al. The adjusted analogy-based software effort estimation based on similarity distances , 2007, J. Syst. Softw..
[43] Zhi-hui Zhan,et al. Renumber strategy enhanced particle swarm optimization for cloud computing resource scheduling , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).
[44] Isabel M. Ramos,et al. An evolutionary approach to estimating software development projects , 2001, Inf. Softw. Technol..
[45] Taghi M. Khoshgoftaar,et al. Using Genetic Programming to Determine Software Quality , 1999, FLAIRS.
[46] 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..
[47] Sun-Jen Huang,et al. Optimization of analogy weights by genetic algorithm for software effort estimation , 2006, Inf. Softw. Technol..
[48] Ruchika Malhotra,et al. Search based techniques for software fault prediction: current trends and future directions , 2014, SBST 2014.
[49] Bart Baesens,et al. Mining software repositories for comprehensible software fault prediction models , 2008, J. Syst. Softw..
[50] Christopher J. Lokan,et al. What should you optimize when building an estimation model? , 2005, 11th IEEE International Software Metrics Symposium (METRICS'05).
[51] Mark Harman,et al. Genetic programming for Reverse Engineering , 2013, 2013 20th Working Conference on Reverse Engineering (WCRE).
[52] Ali Selamat,et al. A survey on software fault detection based on different prediction approaches , 2014, Vietnam Journal of Computer Science.
[53] Cong Jin,et al. Software Fault Prediction Model Based on Adaptive Dynamical and Median Particle Swarm Optimization , 2010, 2010 Second International Conference on Multimedia and Information Technology.
[54] Lionel C. Briand,et al. A practical guide for using statistical tests to assess randomized algorithms in software engineering , 2011, 2011 33rd International Conference on Software Engineering (ICSE).
[55] Linda Di Geronimo,et al. A Parallel Genetic Algorithm Based on Hadoop MapReduce for the Automatic Generation of JUnit Test Suites , 2012, 2012 IEEE Fifth International Conference on Software Testing, Verification and Validation.
[56] Kaushal K. Shukla,et al. Neuro-genetic prediction of software development effort , 2000, Inf. Softw. Technol..
[57] Rodrigo A. Vivanco,et al. Finding Effective Software Metrics to Classify Maintainability Using a Parallel Genetic Algorithm , 2004, GECCO.
[58] Aurora Trinidad Ramirez Pozo,et al. A symbolic fault-prediction model based on multiobjective particle swarm optimization , 2010, J. Syst. Softw..
[59] H. E. Dunsmore,et al. Software engineering metrics and models , 1986 .
[60] Thong Ngee Goh,et al. A study of mutual information based feature selection for case based reasoning in software cost estimation , 2009, Expert Syst. Appl..
[61] 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..
[62] Taghi M. Khoshgoftaar,et al. Using the genetic algorithm to build optimal neural networks for fault-prone module detection , 1996, Proceedings of ISSRE '96: 7th International Symposium on Software Reliability Engineering.
[63] Georgios Dounias,et al. Application of Genetic Programming in Software Engineering Empirical Data Modelling , 2008, ICSOFT.
[64] Silvio Romero de Lemos Meira,et al. A GA-based feature selection and parameters optimization for support vector regression applied to software effort estimation , 2008, SAC '08.
[65] Filomena Ferrucci,et al. A Genetic Algorithm to Configure Support Vector Machines for Predicting Fault-Prone Components , 2011, PROFES.
[66] Akito Monden,et al. Using search-based metric selection and oversampling to predict fault prone modules , 2010, CCECE 2010.
[67] Emilia Mendes,et al. Using tabu search to configure support vector regression for effort estimation , 2013, Empirical Software Engineering.
[68] Mark Harman,et al. Search Based Software Engineering: Techniques, Taxonomy, Tutorial , 2010, LASER Summer School.
[69] Aurora Trinidad Ramirez Pozo,et al. Exploring machine learning techniques for software size estimation , 2003, 23rd International Conference of the Chilean Computer Science Society, 2003. SCCC 2003. Proceedings..
[70] Santanu Kumar Rath,et al. Validating the Effectiveness of Object-Oriented Metrics for Predicting Maintainability☆ , 2015 .
[71] Xiuzhen Zhang,et al. Comments on "Data Mining Static Code Attributes to Learn Defect Predictors" , 2007, IEEE Trans. Software Eng..
[72] 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.
[73] Mark Harman,et al. Search-Based Software Project Management , 2014, Software Project Management in a Changing World.
[74] John A. Clark,et al. Formulating software engineering as a search problem , 2003, IEE Proc. Softw..
[75] Ruchika Malhotra,et al. A systematic review of machine learning techniques for software fault prediction , 2015, Appl. Soft Comput..
[76] Arturo Chavoya,et al. Applying Genetic Programming for Estimating Software Development Effort of Short-scale Projects , 2011, 2011 Eighth International Conference on Information Technology: New Generations.
[77] Xin Yao,et al. Software effort estimation as a multiobjective learning problem , 2013, TSEM.
[78] Tarun Kumar Rawat,et al. Optimal fractional delay-IIR filter design using cuckoo search algorithm. , 2015, ISA transactions.
[79] Filomena Ferrucci,et al. Single and Multi Objective Genetic Programming for software development effort estimation , 2012, SAC '12.
[80] Mark Harman,et al. Less is More: Temporal Fault Predictive Performance over Multiple Hadoop Releases , 2014, SSBSE.
[81] Ruchika Malhotra,et al. Mining the impact of object oriented metrics for change prediction using Machine Learning and Search-based techniques , 2015, 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI).
[82] Farhad Soleimanian Gharehchopogh,et al. A new approach by using Tabu search and genetic algorithms in Software Cost estimation , 2015, 2015 9th International Conference on Application of Information and Communication Technologies (AICT).
[83] Mark Harman,et al. Why the Virtual Nature of Software Makes It Ideal for Search Based Optimization , 2010, FASE.
[84] Gerardo Canfora,et al. Multi-objective Cross-Project Defect Prediction , 2013, 2013 IEEE Sixth International Conference on Software Testing, Verification and Validation.
[85] Filomena Ferrucci,et al. A further analysis on the use of Genetic Algorithm to configure Support Vector Machines for inter-release fault prediction , 2012, SAC '12.
[86] Wasif Afzal,et al. Using Faults-Slip-Through Metric as a Predictor of Fault-Proneness , 2010, 2010 Asia Pacific Software Engineering Conference.
[87] Nan-Hsing Chiu,et al. Combining techniques for software quality classification: An integrated decision network approach , 2011, Expert Syst. Appl..
[88] Ruchika Malhotra,et al. The Ability of Search-Based Algorithms to Predict Change-Prone Classes , 2014 .
[89] Zhang Dan. Improving the accuracy in software effort estimation: Using artificial neural network model based on particle swarm optimization , 2013, Proceedings of 2013 IEEE International Conference on Service Operations and Logistics, and Informatics.
[90] Tarun Kumar Sharma,et al. Halton Based Initial Distribution in Artificial Bee Colony Algorithm and Its Application in Software Effort Estimation , 2011, 2011 Sixth International Conference on Bio-Inspired Computing: Theories and Applications.
[91] Tarun Kumar Rawat,et al. Optimal design of FIR fractional order differentiator using cuckoo search algorithm , 2015, Expert Syst. Appl..
[92] Martin J. Shepperd,et al. Using Genetic Programming to Improve Software Effort Estimation Based on General Data Sets , 2003, GECCO.
[93] Taghi M. Khoshgoftaar,et al. Genetic programming-based decision trees for software quality classification , 2003, Proceedings. 15th IEEE International Conference on Tools with Artificial Intelligence.
[94] Chris F. Kemerer,et al. A Metrics Suite for Object Oriented Design , 2015, IEEE Trans. Software Eng..
[95] Phil McMinn,et al. Search‐based software test data generation: a survey , 2004, Softw. Test. Verification Reliab..
[96] Ruchika Malhotra,et al. Comparative analysis of statistical and machine learning methods for predicting faulty modules , 2014, Appl. Soft Comput..
[97] Parag C. Pendharkar,et al. Exhaustive and heuristic search approaches for learning a software defect prediction model , 2010, Eng. Appl. Artif. Intell..
[98] David R. White,et al. Cloud Computing and SBSE , 2013, SSBSE.
[99] Siti Zaiton Mohd Hashim,et al. A PSO-based model to increase the accuracy of software development effort estimation , 2012, Software Quality Journal.
[100] Mark Harman,et al. Search-based software engineering , 2001, Inf. Softw. Technol..
[101] Ivar Jacobson,et al. Object-Oriented Software Engineering , 1991, TOOLS.
[102] Wasif Afzal,et al. On the application of genetic programming for software engineering predictive modeling: A systematic review , 2011, Expert Syst. Appl..
[103] Alaa F. Sheta,et al. Evaluating software cost estimation models using particle swarm optimisation and fuzzy logic for NASA projects: a comparative study , 2010, Int. J. Bio Inspired Comput..
[104] Emilia Mendes,et al. Investigating Tabu Search for Web Effort Estimation , 2010, 2010 36th EUROMICRO Conference on Software Engineering and Advanced Applications.
[105] J.K. Chhabra,et al. Use of genetic algorithm for software maintainability metrics' conditioning , 2007, 15th International Conference on Advanced Computing and Communications (ADCOM 2007).
[106] Li-Wei Chen,et al. Integration of the grey relational analysis with genetic algorithm for software effort estimation , 2008, Eur. J. Oper. Res..
[107] Bart Baesens,et al. Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings , 2008, IEEE Transactions on Software Engineering.
[108] Yong Hu,et al. Systematic literature review of machine learning based software development effort estimation models , 2012, Inf. Softw. Technol..
[109] H. Zulzalil,et al. Applying evolution programming Search Based Software Engineering (SBSE) in selecting the best open source software maintainability metrics , 2012, 2012 International Symposium on Computer Applications and Industrial Electronics (ISCAIE).
[110] Filomena Ferrucci,et al. Estimating Software Development Effort using Tabu Search , 2010, ICEIS.
[111] Ruchika Malhotra,et al. Fault Prediction Using Statistical and Machine Learning Methods for Improving Software Quality , 2012, J. Inf. Process. Syst..
[112] José Javier Dolado,et al. A Validation of the Component-Based Method for Software Size Estimation , 2000, IEEE Trans. Software Eng..
[113] Mohammad Alshayeb,et al. Hybrid Intelligent Model for Software Maintenance Prediction , 2013 .
[114] Zhenyou Li. Intelligently Predict Project Effort by Reduced Models Based on Multiple Regressions and Genetic Algorithms with Neural Networks , 2010, 2010 International Conference on E-Business and E-Government.
[115] Ajith Abraham,et al. Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews , 2007 .
[116] Danielle Azar. A Genetic Algorithm for Improving Accuracy of Software Quality Predictive Models: a Search-Based Software Engineering Approach , 2010, Int. J. Comput. Intell. Appl..
[117] Rodrigo C. Barros,et al. Predicting software maintenance effort through evolutionary-based decision trees , 2012, SAC '12.
[118] Banu Diri,et al. Investigating the effect of dataset size, metrics sets, and feature selection techniques on software fault prediction problem , 2009, Inf. Sci..
[119] Taghi M. Khoshgoftaar,et al. Evolutionary Optimization of Software Quality Modeling with Multiple Repositories , 2010, IEEE Transactions on Software Engineering.
[120] Georgios Dounias,et al. Deriving Models for Software Project Effort Estimation by Means of Genetic Programming , 2009, KDIR.
[121] Danielle Azar,et al. An ant colony optimization algorithm to improve software quality prediction models: Case of class stability , 2011, Inf. Softw. Technol..
[122] Taghi M. Khoshgoftaar,et al. Evolutionary neural networks: a robust approach to software reliability problems , 1997, Proceedings The Eighth International Symposium on Software Reliability Engineering.
[123] Alaa F. Sheta,et al. A GP effort estimation model utilizing line of code and methodology for NASA software projects , 2010, 2010 10th International Conference on Intelligent Systems Design and Applications.