“Sampling” as a Baseline Optimizer for Search-Based Software Engineering
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Tim Menzies | Vivek Nair | Jianfeng Chen | Rahul Krishna | T. Menzies | V. Nair | Jianfeng Chen | R. Krishna
[1] Thomas Jansen,et al. On the analysis of the (1+1) evolutionary algorithm , 2002, Theor. Comput. Sci..
[2] A. Keane,et al. Evolutionary Optimization of Computationally Expensive Problems via Surrogate Modeling , 2003 .
[3] Sven Kosub,et al. A note on the triangle inequality for the Jaccard distance , 2016, Pattern Recognit. Lett..
[4] Yuanyuan Zhang,et al. Search based software engineering for software product line engineering: a survey and directions for future work , 2014, SPLC.
[5] Robert C. Holte,et al. Very Simple Classification Rules Perform Well on Most Commonly Used Datasets , 1993, Machine Learning.
[6] Gunter Saake,et al. Predicting performance via automated feature-interaction detection , 2012, 2012 34th International Conference on Software Engineering (ICSE).
[7] Marijn J. H. Heule,et al. SAT Competition 2016: Recent Developments , 2017, AAAI.
[8] Yan Li,et al. A Practical Guide to Select Quality Indicators for Assessing Pareto-Based Search Algorithms in Search-Based Software Engineering , 2016, 2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE).
[9] Tim Menzies,et al. Using Simulation to Investigate Requirements Prioritization Strategies , 2008, 2008 23rd IEEE/ACM International Conference on Automated Software Engineering.
[10] Mark Harman,et al. Not going to take this anymore: Multi-objective overtime planning for Software Engineering projects , 2013, 2013 35th International Conference on Software Engineering (ICSE).
[11] Qingfu Zhang,et al. MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.
[12] Tim Menzies,et al. RIOT: A Stochastic-Based Method for Workflow Scheduling in the Cloud , 2017, 2018 IEEE 11th International Conference on Cloud Computing (CLOUD).
[13] Kalyanmoy Deb,et al. A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.
[14] Yoshua Bengio,et al. Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..
[15] Armin Biere,et al. SAT Race 2015 , 2016, Artif. Intell..
[16] Tim Menzies,et al. Beyond evolutionary algorithms for search-based software engineering , 2017, Inf. Softw. Technol..
[17] Guilherme Horta Travassos,et al. Cross versus Within-Company Cost Estimation Studies: A Systematic Review , 2007, IEEE Transactions on Software Engineering.
[18] DorigoMarco,et al. A survey on metaheuristics for stochastic combinatorial optimization , 2009 .
[19] Tim Menzies,et al. Learning Mitigations for Pilot Issues When Landing Aircraft (via Multiobjective Optimization and Multiagent Simulations) , 2016, IEEE Transactions on Human-Machine Systems.
[20] Stephen F. Smith,et al. Modeling GA Performance for Control Parameter Optimization , 2000, GECCO.
[21] Mark Harman,et al. Searching for better configurations: a rigorous approach to clone evaluation , 2013, ESEC/FSE 2013.
[22] Arnaud Gotlieb,et al. Minimizing test suites in software product lines using weight-based genetic algorithms , 2013, GECCO '13.
[23] Bojan Cukic,et al. An alternative to model checking: verification by random search of AND-OR graphs representing finite-state models , 2002, 7th IEEE International Symposium on High Assurance Systems Engineering, 2002. Proceedings..
[24] Tim Menzies,et al. Learning the Task Management Space of an Aircraft Approach Model , 2014, AAAI Spring Symposia.
[25] Barry W. Boehm,et al. The business case for automated software engineering , 2007, ASE.
[26] Barry W. Boehm,et al. Using Risk to Balance Agile and Plan-Driven Methods , 2003, Computer.
[27] Krzysztof Czarnecki,et al. A Study of Variability Models and Languages in the Systems Software Domain , 2013, IEEE Transactions on Software Engineering.
[28] Andreas Krause,et al. Active Learning for Multi-Objective Optimization , 2013, ICML.
[29] David A. Van Veldhuizen,et al. Evolutionary Computation and Convergence to a Pareto Front , 1998 .
[30] Joseph Krall,et al. Faster Evolutionary Multi-Objective Optimization via GALE, the Geometric Active Learner , 2014 .
[31] Sanjoy Dasgupta,et al. Random projection trees and low dimensional manifolds , 2008, STOC.
[32] Wolfgang Banzhaf,et al. Genetic Programming: An Introduction , 1997 .
[33] Tim Menzies,et al. Easy over hard: a case study on deep learning , 2017, ESEC/SIGSOFT FSE.
[34] Qingfu Zhang,et al. Combining Model-based and Genetics-based Offspring Generation for Multi-objective Optimization Using a Convergence Criterion , 2006, 2006 IEEE International Conference on Evolutionary Computation.
[35] Barry W. Boehm,et al. Accurate estimates without local data? , 2009, Softw. Process. Improv. Pract..
[36] Emmanuel Letier,et al. Understanding clusters of optimal solutions in multi-objective decision problems , 2011, 2011 IEEE 19th International Requirements Engineering Conference.
[37] Claire Le Goues,et al. Improved Crossover Operators for Genetic Programming for Program Repair , 2016, SSBSE.
[38] Tim Menzies,et al. Applications of abduction: hypothesis testing of neuroendocrinological qualitative compartmental models , 1997, Artif. Intell. Medicine.
[39] Tim Menzies,et al. An (Accidental) Exploration of Alternatives to Evolutionary Algorithms for SBSE , 2016, SSBSE.
[40] Rainer Storn,et al. Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..
[41] Sean Quan Lau. Domain Analysis of E-Commerce Systems Using Feature-Based Model Templates , 2006 .
[42] Ronald L. Rivest,et al. Introduction to Algorithms , 1990 .
[43] Donald D. Cowan,et al. Decision-making coordination in collaborative product configuration , 2008, SAC '08.
[44] Shane McIntosh,et al. Automated Parameter Optimization of Classification Techniques for Defect Prediction Models , 2016, 2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE).
[45] S. She,et al. Variability Modeling in the Systems Software Domain , 2012 .
[46] Paul R. Cohen,et al. Empirical methods for artificial intelligence , 1995, IEEE Expert.
[47] Sergio Segura,et al. SIP: Optimal Product Selection from Feature Models Using Many-Objective Evolutionary Optimization , 2016, ACM Trans. Softw. Eng. Methodol..
[48] Chih-Jen Lin,et al. Radius Margin Bounds for Support Vector Machines with the RBF Kernel , 2002, Neural Computation.
[49] Krzysztof Czarnecki,et al. Reverse engineering feature models , 2011, 2011 33rd International Conference on Software Engineering (ICSE).
[50] Lefteris Angelis,et al. Ranking and Clustering Software Cost Estimation Models through a Multiple Comparisons Algorithm , 2013, IEEE Transactions on Software Engineering.
[51] Peter A. Whigham,et al. A Baseline Model for Software Effort Estimation , 2015, TSEM.
[52] Marco Laumanns,et al. Scalable Test Problems for Evolutionary Multiobjective Optimization , 2005, Evolutionary Multiobjective Optimization.
[53] Tim Menzies,et al. GALE: Geometric Active Learning for Search-Based Software Engineering , 2015, IEEE Transactions on Software Engineering.
[54] Tim Menzies,et al. XOMO: Understanding Development Options for Autonomy , 2005 .
[55] Marc Parizeau,et al. DEAP: evolutionary algorithms made easy , 2012, J. Mach. Learn. Res..
[56] Abdel Salam Sayyad,et al. Pareto-optimal search-based software engineering (POSBSE): A literature survey , 2013, 2013 2nd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE).
[57] Erick Cantú-Paz,et al. Adaptive Sampling for Noisy Problems , 2004, GECCO.
[58] Stephen G. MacDonell,et al. Evaluating prediction systems in software project estimation , 2012, Inf. Softw. Technol..
[59] Ping Wang,et al. Optimal control based regression test selection for service-oriented workflow applications , 2017, J. Syst. Softw..
[60] Tim Menzies,et al. RIOT: a Novel Stochastic Method for Rapidly Configuring Cloud-Based Workflows , 2017, ArXiv.
[61] Yves Le Traon,et al. Combining Multi-Objective Search and Constraint Solving for Configuring Large Software Product Lines , 2015, 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering.
[62] Shane McIntosh,et al. Revisiting the Impact of Classification Techniques on the Performance of Defect Prediction Models , 2015, 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering.
[63] David H. Wolpert,et al. No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..
[64] Barry W. Boehm,et al. How to avoid drastic software process change (using stochastic stability) , 2009, 2009 IEEE 31st International Conference on Software Engineering.
[65] Tim Menzies,et al. Tuning for Software Analytics: is it Really Necessary? , 2016, Inf. Softw. Technol..
[66] Dan Boneh,et al. On genetic algorithms , 1995, COLT '95.
[67] Mark Harman,et al. An empirical study of the robustness of two module clustering fitness functions , 2005, GECCO '05.
[68] Katsuro Inoue,et al. Search-based software library recommendation using multi-objective optimization , 2017, Inf. Softw. Technol..
[69] Christos Faloutsos,et al. FastMap: a fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets , 1995, SIGMOD '95.
[70] Danilo Ardagna,et al. A Multi-model Optimization Framework for the Model Driven Design of Cloud Applications , 2014, SSBSE.
[71] R. Tibshirani,et al. An introduction to the bootstrap , 1993 .
[72] Lothar Thiele,et al. Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..
[73] 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).
[74] Naveen Kumar Lekkalapudi. Cross Trees: Visualizing Estimations using Decision Trees , 2014 .
[75] Mark Harman,et al. GPGPU test suite minimisation: search based software engineering performance improvement using graphics cards , 2013, Empirical Software Engineering.
[76] Bojan Cukic,et al. Caveats , 2020, The African Continental Free Trade Area: Economic and Distributional Effects.
[77] Gordon Fraser,et al. Parameter tuning or default values? An empirical investigation in search-based software engineering , 2013, Empirical Software Engineering.
[78] Kalyanmoy Deb,et al. A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..
[79] LinChih-Jen,et al. Radius margin bounds for support vector machines with the RBF kernel , 2003 .
[80] Goldberg,et al. Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.
[81] Mark Harman,et al. Adaptive Multi-Objective Evolutionary Algorithms for Overtime Planning in Software Projects , 2017, IEEE Transactions on Software Engineering.
[82] Robert F. Cohen,et al. Applications of Abduction: Testing Very Long Qualitative Simulations , 2002, IEEE Trans. Knowl. Data Eng..
[83] Mark Harman,et al. Less is More: Temporal Fault Predictive Performance over Multiple Hadoop Releases , 2014, SSBSE.
[84] John Platt,et al. FastMap, MetricMap, and Landmark MDS are all Nystrom Algorithms , 2005, AISTATS.
[85] Pedro M. Domingos. A few useful things to know about machine learning , 2012, Commun. ACM.
[86] 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).
[87] Risto Miikkulainen,et al. Estimating the Advantage of Age-Layering in Evolutionary Algorithms , 2016, GECCO.
[88] Luca Maria Gambardella,et al. A survey on metaheuristics for stochastic combinatorial optimization , 2009, Natural Computing.
[89] Eckart Zitzler,et al. Indicator-Based Selection in Multiobjective Search , 2004, PPSN.
[90] Enrique Alba,et al. SMPSO: A new PSO-based metaheuristic for multi-objective optimization , 2009, 2009 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making(MCDM).
[91] Ellis Horowitz,et al. Software Cost Estimation with COCOMO II , 2000 .
[92] Bojan Cukic,et al. What makes finite-state models more (or less) testable? , 2002, Proceedings 17th IEEE International Conference on Automated Software Engineering,.
[93] A. Vargha,et al. A Critique and Improvement of the CL Common Language Effect Size Statistics of McGraw and Wong , 2000 .