Sharing Data and Models in Software Engineering
暂无分享,去创建一个
Burak Turhan | Leandro L. Minku | Fayola Peters | Ekrem Kocaguneli | Tim Menzies | T. Menzies | Fayola Peters | Burak Turhan | Ekrem Kocaguneli
[1] Ramesh Nallapati,et al. A Comparative Study of Methods for Transductive Transfer Learning , 2007 .
[2] Tim Menzies,et al. Distributed development considered harmful? , 2013, 2013 35th International Conference on Software Engineering (ICSE).
[3] Jacek Czerwonka,et al. CRANE: Failure Prediction, Change Analysis and Test Prioritization in Practice -- Experiences from Windows , 2011, 2011 Fourth IEEE International Conference on Software Testing, Verification and Validation.
[4] Xin Yao,et al. DDD: A New Ensemble Approach for Dealing with Concept Drift , 2012, IEEE Transactions on Knowledge and Data Engineering.
[5] Yücel Saygin,et al. Suppressing microdata to prevent classification based inference , 2009, The VLDB Journal.
[6] Marcus A. Maloof,et al. Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts , 2007, J. Mach. Learn. Res..
[7] Rayid Ghani,et al. Testing software in age of data privacy: a balancing act , 2011, ESEC/FSE '11.
[8] Miguel Castro,et al. Better bug reporting with better privacy , 2008, ASPLOS 2008.
[9] Tim Menzies,et al. Just enough learning (of association rules): the TAR2 “Treatment” learner , 2006, Artificial Intelligence Review.
[10] Silvio Romero de Lemos Meira,et al. Bagging Predictors for Estimation of Software Project Effort , 2007, 2007 International Joint Conference on Neural Networks.
[11] Emilia Mendes,et al. Bayesian Network Models for Web Effort Prediction: A Comparative Study , 2008, IEEE Transactions on Software Engineering.
[12] Ayse Basar Bener,et al. Ensemble of neural networks with associative memory (ENNA) for estimating software development costs , 2009, Knowl. Based Syst..
[13] David Lo,et al. kb-anonymity: a model for anonymized behaviour-preserving test and debugging data , 2011, PLDI '11.
[14] Russell Reed,et al. Pruning algorithms-a survey , 1993, IEEE Trans. Neural Networks.
[15] Ye Yang,et al. An investigation on the feasibility of cross-project defect prediction , 2012, Automated Software Engineering.
[16] Elaine J. Weyuker,et al. Programmer-based fault prediction , 2010, PROMISE '10.
[17] Daniel Ryan Baker,et al. A Hybrid Approach to Expert and Model Based Effort Estimation , 2007 .
[18] José Javier Dolado,et al. On the problem of the software cost function , 2001, Inf. Softw. Technol..
[19] S. Arun Kumar,et al. State of Software Metrics to Forecast Variety of Elements in the Software Development Process , 2011 .
[20] Forrest Shull,et al. How perspective-based reading can improve requirements inspections , 2000, Computer.
[21] John E. Gaffney,et al. Software Function, Source Lines of Code, and Development Effort Prediction: A Software Science Validation , 1983, IEEE Transactions on Software Engineering.
[22] Ayse Basar Bener,et al. Practical considerations in deploying statistical methods for defect prediction: A case study within the Turkish telecommunications industry , 2010, Inf. Softw. Technol..
[23] Tim Menzies,et al. Data Mining Static Code Attributes to Learn Defect Predictors , 2007, IEEE Transactions on Software Engineering.
[24] Y. Miyazaki,et al. Robust regression for developing software estimation models , 1994, J. Syst. Softw..
[25] Carla E. Brodley,et al. Active learning for biomedical citation screening , 2010, KDD.
[26] Tim Menzies,et al. Goldfish bowl panel: Software development analytics , 2012, 2012 34th International Conference on Software Engineering (ICSE).
[27] Tibor Gyimóthy,et al. Empirical validation of object-oriented metrics on open source software for fault prediction , 2005, IEEE Transactions on Software Engineering.
[28] 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.
[29] Ninghui Li,et al. Injector: Mining Background Knowledge for Data Anonymization , 2008, 2008 IEEE 24th International Conference on Data Engineering.
[30] Ricardo Valerdi,et al. Heuristics for Systems Engineering Cost Estimation , 2011, IEEE Systems Journal.
[31] Victor R. Basili,et al. A Validation of Object-Oriented Design Metrics as Quality Indicators , 1996, IEEE Trans. Software Eng..
[32] Witold Pedrycz,et al. Practical assessment of the models for identification of defect-prone classes in object-oriented commercial systems using design metrics , 2003, J. Syst. Softw..
[33] Ayse Basar Bener,et al. Regularities in Learning Defect Predictors , 2010, PROFES.
[34] Vasilios Zorkadis,et al. Efficient information theoretic strategies for classifier combination, feature extraction and performance evaluation in improving false positives and false negatives for spam e-mail filtering , 2005, Neural Networks.
[35] H. B. Mann,et al. On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other , 1947 .
[36] Roger C. Schank,et al. Scripts, plans, goals and understanding: an inquiry into human knowledge structures , 1978 .
[37] Günther Ruhe,et al. Analysis of attribute weighting heuristics for analogy-based software effort estimation method AQUA+ , 2008, Empirical Software Engineering.
[38] Xin Yao,et al. journal homepage: www.elsevier.com/locate/infsof Ensembles and locality: Insight on improving software effort estimation , 2022 .
[39] Giovanni Seni,et al. Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions , 2010, Ensemble Methods in Data Mining.
[40] Lionel C. Briand,et al. Predicting fault-prone components in a java legacy system , 2006, ISESE '06.
[41] Witold Pedrycz,et al. Identification of defect-prone classes in telecommunication software systems using design metrics , 2006, Inf. Sci..
[42] Barry W. Boehm,et al. Can we build software faster and better and cheaper? , 2009, PROMISE '09.
[43] B. John Oommen,et al. A brief taxonomy and ranking of creative prototype reduction schemes , 2003, Pattern Analysis & Applications.
[44] Steven R. Rakitin,et al. Software verification and validation for practitioners and managers , 2001 .
[45] Emilia Mendes,et al. Applying moving windows to software effort estimation , 2009, ESEM 2009.
[46] Sargur N. Srihari,et al. Decision Combination in Multiple Classifier Systems , 1994, IEEE Trans. Pattern Anal. Mach. Intell..
[47] Michelle Cartwright,et al. An Empirical Investigation of an Object-Oriented Software System , 2000, IEEE Trans. Software Eng..
[48] Chidchanok Lursinsap,et al. Estimating Software Effort with Minimum Features Using Neural Functional Approximation , 2010, 2010 International Conference on Computational Science and Its Applications.
[49] Arvinder Kaur,et al. Empirical analysis for investigating the effect of object-oriented metrics on fault proneness: a replicated case study , 2009 .
[50] Forrest Shull,et al. Local versus Global Lessons for Defect Prediction and Effort Estimation , 2013, IEEE Transactions on Software Engineering.
[51] Charles Elkan,et al. Scalability for clustering algorithms revisited , 2000, SKDD.
[52] Jiri Matas,et al. On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[53] Ayse Basar Bener,et al. A new perspective on data homogeneity in software cost estimation: a study in the embedded systems domain , 2010, Software Quality Journal.
[54] L. Breiman. Heuristics of instability and stabilization in model selection , 1996 .
[55] Ayse Basar Bener,et al. On the relative value of cross-company and within-company data for defect prediction , 2009, Empirical Software Engineering.
[56] Tim Menzies,et al. Finding conclusion stability for selecting the best effort predictor in software effort estimation , 2012, Automated Software Engineering.
[57] Barry W. Boehm,et al. Software development cost estimation approaches — A survey , 2000, Ann. Softw. Eng..
[58] A. E. Eiben,et al. Introduction to Evolutionary Computing , 2003, Natural Computing Series.
[59] Emilia Mendes,et al. Investigating the Use of Chronological Splitting to Compare Software Cross-company and Single-company Effort Predictions: A Replicated Study , 2009, EASE.
[60] Kyuseok Shim,et al. Approximate algorithms with generalizing attribute values for k-anonymity , 2010, Inf. Syst..
[61] Chen Fu,et al. Is Data Privacy Always Good for Software Testing? , 2010, 2010 IEEE 21st International Symposium on Software Reliability Engineering.
[62] Xin Yao,et al. Can cross-company data improve performance in software effort estimation? , 2012, PROMISE '12.
[63] Anas N. Al-Rabadi,et al. A comparison of modified reconstructability analysis and Ashenhurst‐Curtis decomposition of Boolean functions , 2004 .
[64] Andres S. Orrego,et al. SAWTOOTH: Learning from Huge Amounts of Data , 2004 .
[65] Xin Yao,et al. How to make best use of cross-company data in software effort estimation? , 2014, ICSE.
[66] Philip S. Yu,et al. Privacy-preserving data publishing: A survey of recent developments , 2010, CSUR.
[67] Taghi M. Khoshgoftaar,et al. Software quality analysis by combining multiple projects and learners , 2008, Software Quality Journal.
[68] Barry W. Boehm,et al. How Much Software Quality Investment Is Enough: A Value-Based Approach , 2006, IEEE Software.
[69] Tim Menzies,et al. Software Analytics: So What? , 2013, IEEE Softw..
[70] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[71] Tim Menzies,et al. Active learning and effort estimation: Finding the essential content of software effort estimation data , 2013, IEEE Transactions on Software Engineering.
[72] Josep Domingo-Ferrer,et al. Hybrid microdata using microaggregation , 2010, Inf. Sci..
[73] Seok-Beom Roh,et al. Design of fuzzy radial basis function-based polynomial neural networks , 2011, Fuzzy Sets Syst..
[74] Khaled El Emam,et al. The Confounding Effect of Class Size on the Validity of Object-Oriented Metrics , 2001, IEEE Trans. Software Eng..
[75] 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.
[76] Filomena Ferrucci,et al. How Multi-Objective Genetic Programming Is Effective for Software Development Effort Estimation? , 2011, SSBSE.
[77] David J. Hand,et al. Protection or Privacy? Data Mining and Personal Data , 2006, PAKDD.
[78] Koichiro Ochimizu,et al. Towards logistic regression models for predicting fault-prone code across software projects , 2009, ESEM 2009.
[79] Tim Menzies,et al. Scalable product line configuration: A straw to break the camel's back , 2013, 2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE).
[80] Ron Kohavi,et al. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.
[81] Magne Jørgensen,et al. Contrasting ideal and realistic conditions as a means to improve judgment-based software development effort estimation , 2011, Inf. Softw. Technol..
[82] Pornsiri Muenchaisri,et al. Predicting Faulty Classes Using Design Metrics with Discriminant Analysis , 2003, Software Engineering Research and Practice.
[83] Barry W. Boehm,et al. Understanding and Controlling Software Costs , 1988, IEEE Trans. Software Eng..
[84] Barbara A. Kitchenham,et al. A Simulation Study of the Model Evaluation Criterion MMRE , 2003, IEEE Trans. Software Eng..
[85] Ron Kohavi,et al. Wrappers for Feature Subset Selection , 1997, Artif. Intell..
[86] Andrew McCallum,et al. Efficient clustering of high-dimensional data sets with application to reference matching , 2000, KDD '00.
[87] Bernhard Schölkopf,et al. Correcting Sample Selection Bias by Unlabeled Data , 2006, NIPS.
[88] John A. Clark,et al. Metrics are fitness functions too , 2004 .
[89] Tim Menzies,et al. Balancing Privacy and Utility in Cross-Company Defect Prediction , 2013, IEEE Transactions on Software Engineering.
[90] José Javier Dolado,et al. A Validation of the Component-Based Method for Software Size Estimation , 2000, IEEE Trans. Software Eng..
[91] Daphne Koller,et al. Learning a meta-level prior for feature relevance from multiple related tasks , 2007, ICML '07.
[92] Martin S. Feather,et al. Model-checking for validation of a fault protection system , 2001, Proceedings Sixth IEEE International Symposium on High Assurance Systems Engineering. Special Topic: Impact of Networking.
[93] Tim Menzies,et al. Simple software cost analysis: safe or unsafe? , 2005, PROMISE '05.
[94] Ian Witten,et al. Data Mining , 2000 .
[95] Tim Menzies,et al. Model-based tests of truisms , 2002, Proceedings 17th IEEE International Conference on Automated Software Engineering,.
[96] K Atkinson,et al. The use of function points to find cost analogies , 1994 .
[97] Lech Madeyski,et al. Towards identifying software project clusters with regard to defect prediction , 2010, PROMISE '10.
[98] Richard Stutzke. Estimating Software-Intensive Systems: Projects, Products, and Processes (Sei Series in Software Engineering) , 2005 .
[99] Cynthia Dwork,et al. Differential Privacy , 2006, ICALP.
[100] Kevin N. Gurney,et al. An introduction to neural networks , 2018 .
[101] Marcel Korte,et al. Confidence in software cost estimation results based on MMRE and PRED , 2008, PROMISE '08.
[102] Greg Hamerly,et al. Making k-means Even Faster , 2010, SDM.
[103] Bojan Cukic,et al. Building a second opinion: learning cross-company data , 2013, PROMISE.
[104] Ashwin Machanavajjhala,et al. Worst-Case Background Knowledge in Privacy , 2006 .
[105] Paolo Nesi,et al. A study on fault-proneness detection of object-oriented systems , 2001, Proceedings Fifth European Conference on Software Maintenance and Reengineering.
[106] Magne Jørgensen,et al. The Impact of Lessons-Learned Sessions on Effort Estimation and Uncertainty Assessments , 2009, IEEE Transactions on Software Engineering.
[107] Zhihao Chen,et al. Validation methods for calibrating software effort models , 2005, Proceedings. 27th International Conference on Software Engineering, 2005. ICSE 2005..
[108] Michael Fagan. Design and Code Inspections to Reduce Errors in Program Development , 1976, IBM Syst. J..
[109] Burak Turhan,et al. On the dataset shift problem in software engineering prediction models , 2011, Empirical Software Engineering.
[110] Daniela E. Damian,et al. Selecting Empirical Methods for Software Engineering Research , 2008, Guide to Advanced Empirical Software Engineering.
[111] Hamid Habibagahi,et al. Estimating Software Productivity and Cost for NASA Projects , 1991 .
[112] Tom DeMarco,et al. Peopleware: Productive Projects and Teams , 1987 .
[113] Tim Menzies,et al. Feature subset selection can improve software cost estimation accuracy , 2005, ACM SIGSOFT Softw. Eng. Notes.
[114] Abram Hindle. Green mining: A methodology of relating software change to power consumption , 2012, 2012 9th IEEE Working Conference on Mining Software Repositories (MSR).
[115] Yuming Zhou,et al. Empirical Analysis of Object-Oriented Design Metrics for Predicting High and Low Severity Faults , 2006, IEEE Transactions on Software Engineering.
[116] Yann-Gaël Guéhéneuc,et al. Recommendation system for design patterns in software development: An DPR overview , 2012, 2012 Third International Workshop on Recommendation Systems for Software Engineering (RSSE).
[117] Ninghui Li,et al. Modeling and Integrating Background Knowledge in Data Anonymization , 2009, 2009 IEEE 25th International Conference on Data Engineering.
[118] Jesús M. González-Barahona,et al. Comparison between SLOCs and number of files as size metrics for software evolution analysis , 2006, Conference on Software Maintenance and Reengineering (CSMR'06).
[119] Kurt Hornik,et al. Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.
[120] Gavin R. Finnie,et al. Estimating software development effort with connectionist models , 1997, Inf. Softw. Technol..
[121] Irit Dinur,et al. Revealing information while preserving privacy , 2003, PODS.
[122] Tore Dybå,et al. A systematic review of effect size in software engineering experiments , 2007, Inf. Softw. Technol..
[123] David G. Stork,et al. Pattern Classification , 1973 .
[124] Thomas J. Ostrand,et al. \{PROMISE\} Repository of empirical software engineering data , 2007 .
[125] Kalyanmoy Deb,et al. A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..
[126] Latanya Sweeney,et al. Achieving k-Anonymity Privacy Protection Using Generalization and Suppression , 2002, Int. J. Uncertain. Fuzziness Knowl. Based Syst..
[127] Jenq-Neng Hwang,et al. Handbook of Neural Network Signal Processing , 2000, IEEE Transactions on Neural Networks.
[128] Tim Menzies,et al. On the Value of Ensemble Effort Estimation , 2012, IEEE Transactions on Software Engineering.
[129] David S. Christensen,et al. Calibrating Software Cost Models to Department of Defense DatabasesA Review of Ten Studies , 1998 .
[130] 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).
[131] Lefteris Angelis,et al. Using Ensembles for Web Effort Estimation , 2013, 2013 ACM / IEEE International Symposium on Empirical Software Engineering and Measurement.
[132] L. Breiman,et al. Submodel selection and evaluation in regression. The X-random case , 1992 .
[133] Guilherme Horta Travassos,et al. Cross versus Within-Company Cost Estimation Studies: A Systematic Review , 2007, IEEE Transactions on Software Engineering.
[134] Maurice V. Wilkes,et al. Memoirs of a Computer Pioneer , 1985 .
[135] Barry Boehm,et al. From Multiple Regression to Bayesian Analysis for Calibrating COCOMO II , 1999 .
[136] Ramanath Subramanyam,et al. Empirical Analysis of CK Metrics for Object-Oriented Design Complexity: Implications for Software Defects , 2003, IEEE Trans. Software Eng..
[137] Xin Yao,et al. Ensemble Learning Using Multi-Objective Evolutionary Algorithms , 2006, J. Math. Model. Algorithms.
[138] Danny Ho,et al. An Empirical Validation of Object-Oriented Design Metrics for Fault Prediction , 2008 .
[139] Ayse Basar Bener,et al. A comparative study for estimating software development effort intervals , 2011, Software Quality Journal.
[140] Mei-Hwa Chen,et al. An empirical study on object-oriented metrics , 1999, Proceedings Sixth International Software Metrics Symposium (Cat. No.PR00403).
[141] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[142] Elaine J. Weyuker,et al. Do too many cooks spoil the broth? Using the number of developers to enhance defect prediction models , 2008, Empirical Software Engineering.
[143] Magne Jørgensen,et al. How large are software cost overruns? A review of the 1994 CHAOS report , 2006, Inf. Softw. Technol..
[144] James C. Bezdek,et al. Some Notes on Twenty One (21) Nearest Prototype Classifiers , 2000, SSPR/SPR.
[145] Xin Yao,et al. An analysis of diversity measures , 2006, Machine Learning.
[146] Martin Shepperd,et al. Case and Feature Subset Selection in Case-Based Software Project Effort Prediction , 2003 .
[147] Alberto Bacchelli,et al. Content classification of development emails , 2012, 2012 34th International Conference on Software Engineering (ICSE).
[148] Barry W. Boehm,et al. How to avoid drastic software process change (using stochastic stability) , 2009, 2009 IEEE 31st International Conference on Software Engineering.
[149] Tim Menzies,et al. Transfer learning in effort estimation , 2015, Empirical Software Engineering.
[150] A. Bifet,et al. Early Drift Detection Method , 2005 .
[151] Tracy Hall,et al. A Systematic Literature Review on Fault Prediction Performance in Software Engineering , 2012, IEEE Transactions on Software Engineering.
[152] Karen T. Lum,et al. Selecting Best Practices for Effort Estimation , 2006, IEEE Transactions on Software Engineering.
[153] Magne Jørgensen,et al. A Systematic Review of Software Development Cost Estimation Studies , 2007 .
[154] Lionel C. Briand,et al. Assessing the Applicability of Fault-Proneness Models Across Object-Oriented Software Projects , 2002, IEEE Trans. Software Eng..
[155] Emilia Mendes,et al. Investigating the use of chronological splitting to compare software cross-company and single-company effort predictions , 2008 .
[156] Bart Selman,et al. Local search strategies for satisfiability testing , 1993, Cliques, Coloring, and Satisfiability.
[157] José Demisio Simões da Silva,et al. Comparison of Artificial Neural Network and Regression Models in Software Effort Estimation , 2007, 2007 International Joint Conference on Neural Networks.
[158] Sanjoy Dasgupta,et al. Analysis of a greedy active learning strategy , 2004, NIPS.
[159] Martin Lukasiewycz,et al. Opt4J: a modular framework for meta-heuristic optimization , 2011, GECCO '11.
[160] Abbas Heiat,et al. Comparison of artificial neural network and regression models for estimating software development effort , 2002, Inf. Softw. Technol..
[161] Wenliang Du,et al. Privacy-MaxEnt: integrating background knowledge in privacy quantification , 2008, SIGMOD Conference.
[162] João Gama,et al. Discretization from data streams: applications to histograms and data mining , 2006, SAC.
[163] Emilia Mendes,et al. A Comparative Study of Cost Estimation Models for Web Hypermedia Applications , 2003, Empirical Software Engineering.
[164] Christos Faloutsos,et al. FastMap: a fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets , 1995, SIGMOD '95.
[165] Yasuhiro Mashiko,et al. Using the GQM Paradigm to Investigate Influential Factors for Software Process Improvement , 1997, J. Syst. Softw..
[166] James M. Rehg,et al. Active learning for automatic classification of software behavior , 2004, ISSTA '04.
[167] 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.
[168] Ron Kohavi,et al. Supervised and Unsupervised Discretization of Continuous Features , 1995, ICML.
[169] Chin-Liang Chang,et al. Finding Prototypes For Nearest Neighbor Classifiers , 1974, IEEE Transactions on Computers.
[170] Chris Verhoef,et al. The rise and fall of the Chaos report figures , 2010, IEEE Software.
[171] T. Wright,et al. Organizational Benchmarking Using the ISBSG Data Repository , 2001, IEEE Softw..
[172] David W. Aha,et al. Instance-Based Learning Algorithms , 1991, Machine Learning.
[173] Ioannis Stamelos,et al. Software productivity and effort prediction with ordinal regression , 2005, Inf. Softw. Technol..
[174] Philip J. Guo,et al. Characterizing and predicting which bugs get fixed: an empirical study of Microsoft Windows , 2010, 2010 ACM/IEEE 32nd International Conference on Software Engineering.
[175] Janet L. Kolodner,et al. Improving Human Decision Making through Case-Based Decision Aiding , 1991, AI Mag..
[176] Yuanyuan Zhang,et al. App store mining and analysis: MSR for app stores , 2012, 2012 9th IEEE Working Conference on Mining Software Repositories (MSR).
[177] Vitaly Shmatikov,et al. The cost of privacy: destruction of data-mining utility in anonymized data publishing , 2008, KDD.
[178] Jane Huffman Hayes,et al. Advancing candidate link generation for requirements tracing: the study of methods , 2006, IEEE Transactions on Software Engineering.
[179] Tim Menzies,et al. Data Mining Static Code Attributes to Learn Defect Predictors , 2007 .
[180] Tim Menzies,et al. On the value of user preferences in search-based software engineering: A case study in software product lines , 2013, 2013 35th International Conference on Software Engineering (ICSE).
[181] Naonori Ueda,et al. Generalization error of ensemble estimators , 1996, Proceedings of International Conference on Neural Networks (ICNN'96).
[182] Harald C. Gall,et al. Cross-project defect prediction: a large scale experiment on data vs. domain vs. process , 2009, ESEC/SIGSOFT FSE.
[183] Xin Yao,et al. Simultaneous training of negatively correlated neural networks in an ensemble , 1999, IEEE Trans. Syst. Man Cybern. Part B.
[184] Michael English,et al. Fault detection and prediction in an open-source software project , 2009, PROMISE '09.
[185] Andreas Zeller,et al. Learning from 6,000 projects: lightweight cross-project anomaly detection , 2010, ISSTA '10.
[186] Kamal A. Ali,et al. On the Link between Error Correlation and Error Reduction in Decision Tree Ensembles , 1995 .
[187] Thong Ngee Goh,et al. A study of project selection and feature weighting for analogy based software cost estimation , 2009, J. Syst. Softw..
[188] Xin Yao,et al. Ensemble learning via negative correlation , 1999, Neural Networks.
[189] Mark Harman,et al. The relationship between search based software engineering and predictive modeling , 2010, PROMISE '10.
[190] Tim Menzies,et al. Converging on the optimal attainment of requirements , 2002, Proceedings IEEE Joint International Conference on Requirements Engineering.
[191] Forrest Shull,et al. Investigating Reading Techniques for Object-Oriented Framework Learning , 2000, IEEE Trans. Software Eng..
[192] Ayse Basar Bener,et al. Practical considerations in deploying AI for defect prediction: a case study within the Turkish telecommunication industry , 2009, PROMISE '09.
[193] Bojan Cukic,et al. An adaptive approach with active learning in software fault prediction , 2012, PROMISE '12.
[194] Jacky W. Keung. Theoretical Maximum Prediction Accuracy for Analogy-Based Software Cost Estimation , 2008, 2008 15th Asia-Pacific Software Engineering Conference.
[195] Federico Girosi,et al. Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[196] Barry Boehm,et al. Software quality analysis: a value-based approach , 2006 .
[197] Ian H. Witten,et al. The WEKA data mining software: an update , 2009, SKDD.
[198] S. Kotsiantis,et al. Discretization Techniques: A recent survey , 2006 .
[199] Barry Boehm,et al. Calibration Approach and Results of the COCOMO II Post- Architecture Model , 1998 .
[200] Barry W. Boehm,et al. Bayesian Analysis of Empirical Software Engineering Cost Models , 1999, IEEE Trans. Software Eng..
[201] Geoff Holmes,et al. Benchmarking Attribute Selection Techniques for Discrete Class Data Mining , 2003, IEEE Trans. Knowl. Data Eng..
[202] Ninghui Li,et al. t-Closeness: Privacy Beyond k-Anonymity and l-Diversity , 2007, 2007 IEEE 23rd International Conference on Data Engineering.
[203] Christian Bird,et al. The inductive software engineering manifesto: principles for industrial data mining , 2011, MALETS '11.
[204] Burak Turhan,et al. Learning Better Inspection Optimization Policies , 2012, Int. J. Softw. Eng. Knowl. Eng..
[205] David E. Goldberg,et al. Genetic Algorithms, Tournament Selection, and the Effects of Noise , 1995, Complex Syst..
[206] Sadaaki Miyamoto,et al. On the Comparison of Some Fuzzy Clustering Methods for Privacy Preserving Data Mining: Towards the Development of Specific Information Loss Measures , 2009, Kybernetika.
[207] Raghu Ramakrishnan,et al. Privacy Skyline: Privacy with Multidimensional Adversarial Knowledge , 2007, VLDB.
[208] Michael E. Fagan. Advances in software inspections , 1986, IEEE Transactions on Software Engineering.
[209] C. Kaynak,et al. Techniques for Combining Multiple Learners , 1998 .
[210] Shyue-Liang Wang,et al. Hiding informative association rule sets , 2007, Expert Syst. Appl..
[211] Huanhuan Chen,et al. Regularized Negative Correlation Learning for Neural Network Ensembles , 2009, IEEE Transactions on Neural Networks.
[212] Pat Langley,et al. Estimating Continuous Distributions in Bayesian Classifiers , 1995, UAI.
[213] Y. Zhao,et al. Comparison of decision tree methods for finding active objects , 2007, 0708.4274.
[214] Ayse Basar Bener,et al. Exploiting the Essential Assumptions of Analogy-Based Effort Estimation , 2012, IEEE Transactions on Software Engineering.
[215] Harry Zhang,et al. Learning weighted naive Bayes with accurate ranking , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).
[216] Peter Tiño,et al. Managing Diversity in Regression Ensembles , 2005, J. Mach. Learn. Res..
[217] Xin Zhang,et al. Adaptive Email Spam Filtering Based on Information Theory , 2007, WISE.
[218] Tim Menzies,et al. Sharing experiments using open‐source software , 2011, Softw. Pract. Exp..
[219] Horst Bunke,et al. Feature selection algorithms for the generation of multiple classifier systems and their application to handwritten word recognition , 2004 .
[220] Emilia Mendes,et al. Investigating the use of chronological split for software effort estimation , 2009, IET Softw..
[221] D. Sculley,et al. Web-scale k-means clustering , 2010, WWW '10.
[222] Stephen G. MacDonell,et al. What accuracy statistics really measure , 2001, IEE Proc. Softw..
[223] Chris Clifton,et al. Privacy-preserving data mining: why, how, and when , 2004, IEEE Security & Privacy Magazine.
[224] Emilia Mendes,et al. Web effort estimation: the value of cross-company data set compared to single-company data set , 2012, PROMISE '12.
[225] Carolyn Mair,et al. The consistency of empirical comparisons of regression and analogy-based software project cost prediction , 2005, 2005 International Symposium on Empirical Software Engineering, 2005..
[226] Tim Menzies,et al. Automatically finding the control variables for complex system behavior , 2010, Automated Software Engineering.
[227] Filomena Ferrucci,et al. Single and Multi Objective Genetic Programming for software development effort estimation , 2012, SAC '12.
[228] Ayse Bener,et al. Evaluation of Feature Extraction Methods on Software Cost Estimation , 2007, ESEM 2007.
[229] L. Hedges,et al. The Handbook of Research Synthesis , 1995 .
[230] Xin Yao,et al. Diversity creation methods: a survey and categorisation , 2004, Inf. Fusion.
[231] Dongmei Zhang,et al. Performance debugging in the large via mining millions of stack traces , 2012, 2012 34th International Conference on Software Engineering (ICSE).
[232] Eric Bauer,et al. An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants , 1999, Machine Learning.
[233] Padhraic Smyth,et al. From Data Mining to Knowledge Discovery in Databases , 1996, AI Mag..
[234] Jacky W. Keung,et al. Empirical evaluation of analogy-x for software cost estimation , 2008, ESEM '08.
[235] Robert Tibshirani,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.
[236] Christopher M. Bishop,et al. Neural networks for pattern recognition , 1995 .
[237] Žliobait . e,et al. Learning under Concept Drift: an Overview , 2010 .
[238] Sherry Stukes,et al. Software Cost Model Calibration , 1998 .
[239] Lionel C. Briand,et al. Replicated Case Studies for Investigating Quality Factors in Object-Oriented Designs , 2001, Empirical Software Engineering.
[240] Oussama El-Rawas,et al. A second look at Faster, Better, Cheaper , 2010, Innovations in Systems and Software Engineering.
[241] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.
[242] Cynthia Dwork,et al. Differential Privacy: A Survey of Results , 2008, TAMC.
[243] Latanya Sweeney,et al. k-Anonymity: A Model for Protecting Privacy , 2002, Int. J. Uncertain. Fuzziness Knowl. Based Syst..
[244] Andrew Begel,et al. Analyze this! 145 questions for data scientists in software engineering , 2013, ICSE.
[245] Hongfang Liu,et al. Theory of relative defect proneness , 2008, Empirical Software Engineering.
[246] Janez Demsar,et al. Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..
[247] Marcus A. Maloof,et al. Using additive expert ensembles to cope with concept drift , 2005, ICML.
[248] Yue Jiang,et al. Techniques for evaluating fault prediction models , 2008, Empirical Software Engineering.
[249] Martin J. Shepperd,et al. Estimating Software Project Effort Using Analogies , 1997, IEEE Trans. Software Eng..
[250] Wei-Yin Loh,et al. Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..
[251] Tzung-Pei Hong,et al. Efficient sanitization of informative association rules , 2008, Expert Syst. Appl..
[252] Shari Lawrence Pfleeger,et al. An empirical study of maintenance and development estimation accuracy , 2002, J. Syst. Softw..
[253] José Francisco Martínez Trinidad,et al. A review of instance selection methods , 2010, Artificial Intelligence Review.
[254] Fayola Peters,et al. CLIFF: Finding Prototypes for Nearest Neighbor Algorithms with Application to Forensic Trace Evidence , 2010 .
[255] Premkumar T. Devanbu,et al. Ecological inference in empirical software engineering , 2011, 2011 26th IEEE/ACM International Conference on Automated Software Engineering (ASE 2011).
[256] Garima Verma,et al. Software Defects and Object Oriented Metrics - An Empirical Analysis , 2010 .
[257] Michael W. Godfrey,et al. "Cloning Considered Harmful" Considered Harmful , 2006, 2006 13th Working Conference on Reverse Engineering.
[258] H. E. Dunsmore,et al. Software engineering metrics and models , 1986 .
[259] 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..
[260] Roland Kuhn,et al. Discriminative Instance Weighting for Domain Adaptation in Statistical Machine Translation , 2010, EMNLP.
[261] Uri Lipowezky. Selection of the optimal prototype subset for 1-NN classification , 1998, Pattern Recognit. Lett..
[262] Tim Menzies,et al. Data Mining for Very Busy People , 2003, Computer.
[263] Barry W. Boehm,et al. An analysis of trends in productivity and cost drivers over years , 2011, Promise '11.
[264] Doo-Hwan Bae,et al. An empirical analysis of software effort estimation with outlier elimination , 2008, PROMISE '08.
[265] Xin Yao,et al. Performance Scaling of Multi-objective Evolutionary Algorithms , 2003, EMO.
[266] Laurie A. Williams,et al. Evaluating Complexity, Code Churn, and Developer Activity Metrics as Indicators of Software Vulnerabilities , 2011, IEEE Transactions on Software Engineering.
[267] Pierangela Samarati,et al. Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression , 1998 .
[268] Amos Storkey,et al. When Training and Test Sets are Different: Characterising Learning Transfer , 2013 .
[269] John A. Hartigan,et al. Clustering Algorithms , 1975 .
[270] Elisa Bertino,et al. State-of-the-art in privacy preserving data mining , 2004, SGMD.
[271] Jacob Cohen,et al. A power primer. , 1992, Psychological bulletin.
[272] Javam C. Machado,et al. The prediction of faulty classes using object-oriented design metrics , 2001, J. Syst. Softw..
[273] Tim Menzies,et al. Size doesn't matter?: on the value of software size features for effort estimation , 2012, PROMISE '12.
[274] Geoffrey I. Webb,et al. Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining , 2009, J. Mach. Learn. Res..
[275] Mohammad Azzeh,et al. Software effort estimation based on optimized model tree , 2011, Promise '11.
[276] Thong Ngee Goh,et al. A study of the non-linear adjustment for analogy based software cost estimation , 2009, Empirical Software Engineering.
[277] Guangchun Luo,et al. Transfer learning for cross-company software defect prediction , 2012, Inf. Softw. Technol..
[278] 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.
[279] Kun Liu,et al. On the Privacy of Euclidean Distance Preserving Data Perturbation , 2009, ArXiv.
[280] Zhi-Hua Zhou,et al. Sample-based software defect prediction with active and semi-supervised learning , 2012, Automated Software Engineering.
[281] Barbara A. Kitchenham,et al. Effort estimation using analogy , 1996, Proceedings of IEEE 18th International Conference on Software Engineering.
[282] David D. Lewis,et al. Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval , 1998, ECML.
[283] F. Wilcoxon. Individual Comparisons by Ranking Methods , 1945 .
[284] Sinno Jialin Pan,et al. Transfer defect learning , 2013, 2013 35th International Conference on Software Engineering (ICSE).
[285] C. Chabris,et al. Gorillas in Our Midst: Sustained Inattentional Blindness for Dynamic Events , 1999, Perception.
[286] Barry Boehm,et al. Bayesian analysis of software cost and quality models , 1999 .
[287] Magne Jørgensen,et al. A review of studies on expert estimation of software development effort , 2004, J. Syst. Softw..
[288] N. Nagappan,et al. Static analysis tools as early indicators of pre-release defect density , 2005, Proceedings. 27th International Conference on Software Engineering, 2005. ICSE 2005..
[289] S. Muthukrishnan,et al. Data streams: algorithms and applications , 2005, SODA '03.
[290] Gregorio Robles,et al. Replicating MSR: A study of the potential replicability of papers published in the Mining Software Repositories proceedings , 2010, 2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010).
[291] Dino Pedreschi,et al. Anonymity preserving pattern discovery , 2008, The VLDB Journal.
[292] Barry W. Boehm,et al. Finding the right data for software cost modeling , 2005, IEEE Software.
[293] Lloyd A. Smith,et al. Practical feature subset selection for machine learning , 1998 .
[294] A. Vargha,et al. A Critique and Improvement of the CL Common Language Effect Size Statistics of McGraw and Wong , 2000 .
[295] Ayse Basar Bener,et al. ENNA: software effort estimation using ensemble of neural networks with associative memory , 2008, SIGSOFT '08/FSE-16.
[296] Tim Menzies,et al. How to Find Relevant Data for Effort Estimation? , 2011, 2011 International Symposium on Empirical Software Engineering and Measurement.
[297] Taghi M. Khoshgoftaar,et al. Evolutionary Optimization of Software Quality Modeling with Multiple Repositories , 2010, IEEE Transactions on Software Engineering.
[298] Thomas G. Dietterich,et al. Improving SVM accuracy by training on auxiliary data sources , 2004, ICML.
[299] Gerald Reif,et al. Supporting developers with natural language queries , 2010, 2010 ACM/IEEE 32nd International Conference on Software Engineering.
[300] Sudha Ram,et al. Constrained cascade generalization of decision trees , 2004, IEEE Transactions on Knowledge and Data Engineering.
[301] Douglas Fisher,et al. Machine Learning Approaches to Estimating Software Development Effort , 1995, IEEE Trans. Software Eng..
[302] Tim Menzies,et al. Privacy and utility for defect prediction: Experiments with MORPH , 2012, 2012 34th International Conference on Software Engineering (ICSE).
[303] Andrew Chin,et al. Differential Privacy as a Response to the Reidentification Threat: The Facebook Advertiser Case Study , 2012 .
[304] Tim Menzies,et al. Optimizing requirements decisions with keys , 2008, PROMISE '08.
[305] Qiang Yang,et al. Transferring Naive Bayes Classifiers for Text Classification , 2007, AAAI.
[306] Jeffrey C. Carver,et al. Knowledge-Sharing Issues in Experimental Software Engineering , 2004, Empirical Software Engineering.
[307] Elaine J. Weyuker,et al. Where the bugs are , 2004, ISSTA '04.
[308] Ethem Alpaydin,et al. Introduction to machine learning , 2004, Adaptive computation and machine learning.
[309] G DietterichThomas. An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees , 2000 .
[310] Kjetil Moløkken-Østvold,et al. A review of software surveys on software effort estimation , 2003, 2003 International Symposium on Empirical Software Engineering, 2003. ISESE 2003. Proceedings..
[311] Raed Shatnawi,et al. The effectiveness of software metrics in identifying error-prone classes in post-release software evolution process , 2008, J. Syst. Softw..
[312] Yue Jiang,et al. Cost Curve Evaluation of Fault Prediction Models , 2008, 2008 19th International Symposium on Software Reliability Engineering (ISSRE).
[313] Chris F. Kemerer,et al. An empirical validation of software cost estimation models , 1987, CACM.
[314] James C. Bezdek,et al. Nearest prototype classifier designs: An experimental study , 2001, Int. J. Intell. Syst..
[315] B. Baskeles,et al. Software effort estimation using machine learning methods , 2007, 2007 22nd international symposium on computer and information sciences.
[316] Qiang Yang,et al. A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.
[317] Lionel C. Briand,et al. Exploring the relationships between design measures and software quality in object-oriented systems , 2000, J. Syst. Softw..
[318] Emilia Mendes,et al. Using Chronological Splitting to Compare Cross- and Single-company Effort Models: Further Investigation , 2009, ACSC.
[319] Vasilios Zorkadis,et al. Efficient information theoretic extraction of higher order features for improving neural network-based spam e-mail categorization , 2006, J. Exp. Theor. Artif. Intell..
[320] Jiawei Han,et al. Knowledge transfer via multiple model local structure mapping , 2008, KDD.
[321] Bart Baesens,et al. Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings , 2008, IEEE Transactions on Software Engineering.
[322] John Platt,et al. FastMap, MetricMap, and Landmark MDS are all Nystrom Algorithms , 2005, AISTATS.
[323] I. M. Alsmadi,et al. Selecting a standard set of attributes for cost estimation of software projects , 2012, 2012 International Conference on Computer, Information and Telecommunication Systems (CITS).
[324] Wei Zhao,et al. Privacy-Preserving Data Mining Systems , 2007, Computer.
[325] Raymond J. Mooney,et al. Mapping and Revising Markov Logic Networks for Transfer Learning , 2007, AAAI.
[326] Xin Yao,et al. A principled evaluation of ensembles of learning machines for software effort estimation , 2011, Promise '11.
[327] Michael Gertz,et al. Mining email social networks , 2006, MSR '06.
[328] Romain Robbes,et al. Software systems as cities: a controlled experiment , 2011, 2011 33rd International Conference on Software Engineering (ICSE).
[329] Xiao-Bai Li,et al. Identity disclosure protection: A data reconstruction approach for privacy-preserving data mining , 2009, Decis. Support Syst..
[330] José Francisco Martínez Trinidad,et al. A new fast prototype selection method based on clustering , 2010, Pattern Analysis and Applications.
[331] Xin Yao,et al. Using unreliable data for creating more reliable online learners , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).
[332] Xin Yao,et al. Multi-Objective Approaches to Optimal Testing Resource Allocation in Modular Software Systems , 2010, IEEE Transactions on Reliability.
[333] Claes Wohlin,et al. Context in industrial software engineering research , 2009, 2009 3rd International Symposium on Empirical Software Engineering and Measurement.
[334] Rachel Harrison,et al. Multiobjective simulation optimisation in software project management , 2011, GECCO '11.
[335] Les Hatton,et al. Does OO Sync with How We Think? , 1998, IEEE Softw..
[336] D. Ross Jeffery,et al. An Empirical Study of Analogy-based Software Effort Estimation , 1999, Empirical Software Engineering.
[337] Lionel C. Briand,et al. An assessment and comparison of common software cost estimation modeling techniques , 1999, Proceedings of the 1999 International Conference on Software Engineering (IEEE Cat. No.99CB37002).
[338] Tim Menzies,et al. When to use data from other projects for effort estimation , 2010, ASE.
[339] Jean-Marc Desharnais,et al. A comparison of software effort estimation techniques: Using function points with neural networks, case-based reasoning and regression models , 1997, J. Syst. Softw..
[340] Bart Baesens,et al. Data Mining Techniques for Software Effort Estimation: A Comparative Study , 2012, IEEE Transactions on Software Engineering.
[341] Geoffrey I. Webb,et al. Discretization for naive-Bayes learning: managing discretization bias and variance , 2008, Machine Learning.
[342] Alessandro Orso,et al. Camouflage: automated anonymization of field data , 2011, 2011 33rd International Conference on Software Engineering (ICSE).
[343] Lawrence O Gostin,et al. Health information privacy. , 1995, Cornell law review.
[344] Martin J. Shepperd,et al. Comparing Software Prediction Techniques Using Simulation , 2001, IEEE Trans. Software Eng..
[345] G Gigerenzer,et al. Reasoning the fast and frugal way: models of bounded rationality. , 1996, Psychological review.
[346] Peter J. Bickel,et al. Maximum Likelihood Estimation of Intrinsic Dimension , 2004, NIPS.
[347] 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..
[348] Oussama El-Rawas,et al. Understanding the Value of Software Engineering Technologies , 2009, 2009 IEEE/ACM International Conference on Automated Software Engineering.
[349] Brendan Murphy. The difficulties of building generic reliability models for software , 2011, Empirical Software Engineering.
[350] Ferdinand Hergert,et al. Improving model selection by nonconvergent methods , 1993, Neural Networks.
[351] Stephen G. MacDonell,et al. Evaluating prediction systems in software project estimation , 2012, Inf. Softw. Technol..
[352] Tomasz Imielinski,et al. Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.
[353] Milde M. S. Lira,et al. Combining Multiple Artificial Neural Networks Using Random Committee to Decide upon Electrical Disturbance Classification , 2007, 2007 International Joint Conference on Neural Networks.
[354] Jr. Frederick P. Brooks,et al. The mythical man-month (anniversary ed.) , 1995 .
[355] Thomas Zimmermann,et al. Information needs for software development analytics , 2012, 2012 34th International Conference on Software Engineering (ICSE).
[356] Arvinder Kaur,et al. Empirical validation of object-oriented metrics for predicting fault proneness models , 2010, Software Quality Journal.
[357] ASHWIN MACHANAVAJJHALA,et al. L-diversity: privacy beyond k-anonymity , 2006, 22nd International Conference on Data Engineering (ICDE'06).
[358] Tim Menzies,et al. Special issue on repeatable results in software engineering prediction , 2012, Empirical Software Engineering.
[359] Barry W. Boehm. Safe and Simple Software Cost Analysis , 2000, IEEE Software.
[360] Xin Yao,et al. Software effort estimation as a multiobjective learning problem , 2013, TSEM.
[361] Emilia Mendes,et al. How effective is Tabu search to configure support vector regression for effort estimation? , 2010, PROMISE '10.
[362] Karen T. Lum,et al. Stable rankings for different effort models , 2010, Automated Software Engineering.