Recommending Faulty Configurations for Interacting Systems Under Test Using Multi-objective Search
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[1] Camille Salinesi,et al. Combining configuration and recommendation to define an interactive product line configuration approach , 2012, ArXiv.
[2] Arnaud Gotlieb,et al. Multi-objective test prioritization in software product line testing: an industrial case study , 2014, SPLC.
[3] Enrique Alba,et al. Design Issues in a Multiobjective Cellular Genetic Algorithm , 2007, EMO.
[4] 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).
[5] 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).
[6] Shuai Wang,et al. Search-based test case implantation for testing untested configurations , 2019, Inf. Softw. Technol..
[7] Jasbir S. Arora,et al. Survey of multi-objective optimization methods for engineering , 2004 .
[8] Shaukat Ali,et al. Search-based decision ordering to facilitate product line engineering of Cyber-Physical System , 2016, 2016 4th International Conference on Model-Driven Engineering and Software Development (MODELSWARD).
[9] Nikolaj Bjørner,et al. Z3: An Efficient SMT Solver , 2008, TACAS.
[10] Paul Grünbacher,et al. A systematic review and an expert survey on capabilities supporting multi product lines , 2012, Inf. Softw. Technol..
[11] Leslie Pérez Cáceres,et al. The irace package: Iterated racing for automatic algorithm configuration , 2016 .
[12] Thomas Stützle,et al. Computational results for an automatically tuned CMA-ES with increasing population size on the CEC’05 benchmark set , 2012, Soft Computing.
[13] Krzysztof Czarnecki,et al. Sample Spaces and Feature Models: There and Back Again , 2008, 2008 12th International Software Product Line Conference.
[14] Jie Tian,et al. Assessing the quality of industrial avionics software: an extensive empirical evaluation , 2017, Empirical Software Engineering.
[15] Enrique Alba,et al. AbYSS: Adapting Scatter Search to Multiobjective Optimization , 2008, IEEE Transactions on Evolutionary Computation.
[16] Guisheng Fan,et al. Combining Constraint Solving with Different MOEAs for Configuring Large Software Product Lines: A Case Study , 2018, 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC).
[17] Arnaud Gotlieb,et al. Automatic selection of test execution plans from a video conferencing system product line , 2012, VARY '12.
[18] Lionel C. Briand,et al. Generating Test Data from OCL Constraints with Search Techniques , 2013, IEEE Transactions on Software Engineering.
[19] B. Kitchenham,et al. Case Studies for Method and Tool Evaluation , 1995, IEEE Softw..
[20] Jacques Klein,et al. Automated and Scalable T-wise Test Case Generation Strategies for Software Product Lines , 2010, 2010 Third International Conference on Software Testing, Verification and Validation.
[21] S. She,et al. Formal Semantics of the Kconfig Language Technical Note , 2010 .
[22] Marco Laumanns,et al. SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .
[23] Gordon Fraser,et al. Parameter tuning or default values? An empirical investigation in search-based software engineering , 2013, Empirical Software Engineering.
[24] Alexander Egyed,et al. A systematic mapping study of search-based software engineering for software product lines , 2015, Inf. Softw. Technol..
[25] Shuai Wang,et al. UPMOA: An improved search algorithm to support user-preference multi-objective optimization , 2015, 2015 IEEE 26th International Symposium on Software Reliability Engineering (ISSRE).
[26] Xavier Lorca,et al. Choco: an Open Source Java Constraint Programming Library , 2008 .
[27] Sebastian Krieter,et al. A feature-based personalized recommender system for product-line configuration , 2016, GPCE.
[28] Krzysztof Czarnecki,et al. Generating range fixes for software configuration , 2012, 2012 34th International Conference on Software Engineering (ICSE).
[29] N. Breslow. A generalized Kruskal-Wallis test for comparing K samples subject to unequal patterns of censorship , 1970 .
[30] Kalyanmoy Deb,et al. A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..
[31] Krzysztof Czarnecki,et al. Mining configuration constraints: static analyses and empirical results , 2014, ICSE.
[32] Myra B. Cohen,et al. Evaluating improvements to a meta-heuristic search for constrained interaction testing , 2011, Empirical Software Engineering.
[33] Krzysztof Czarnecki,et al. Range Fixes: Interactive Error Resolution for Software Configuration , 2015, IEEE Transactions on Software Engineering.
[34] Magnus C. Ohlsson,et al. Experimentation in Software Engineering , 2000, The Kluwer International Series in Software Engineering.
[35] He Jiang,et al. Feature based problem hardness understanding for requirements engineering , 2017, Science China Information Sciences.
[36] Claes Wohlin,et al. Experimentation in software engineering: an introduction , 2000 .
[37] Eckart Zitzler,et al. Indicator-Based Selection in Multiobjective Search , 2004, PPSN.
[38] Masahiro Tanaka,et al. GA-based decision support system for multicriteria optimization , 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.
[39] Bran Selic,et al. SimPL: A product-line modeling methodology for families of integrated control systems , 2013, Inf. Softw. Technol..
[40] Mark Harman,et al. Pareto efficient multi-objective test case selection , 2007, ISSTA '07.
[41] Li Zhang,et al. Model-based incremental conformance checking to enable interactive product configuration , 2016, Inf. Softw. Technol..
[42] Thorsten Berger,et al. Tackling Combinatorial Explosion: A Study of Industrial Needs and Practices for Analyzing Highly Configurable Systems , 2018, 2018 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE).
[43] Shuai Wang,et al. CBGA-ES+: A Cluster-Based Genetic Algorithm with Non-Dominated Elitist Selection for Supporting Multi-Objective Test Optimization , 2018, IEEE Transactions on Software Engineering.
[44] Arnaud Gotlieb,et al. Cost-effective test suite minimization in product lines using search techniques , 2015, J. Syst. Softw..
[45] Leslie Pérez Cáceres,et al. Ant colony optimization on a limited budget of evaluations , 2015, Swarm Intelligence.
[46] A. Vargha,et al. A Critique and Improvement of the CL Common Language Effect Size Statistics of McGraw and Wong , 2000 .
[47] Xin Yao,et al. Software Module Clustering as a Multi-Objective Search Problem , 2011, IEEE Transactions on Software Engineering.
[48] Øystein Haugen,et al. An algorithm for generating t-wise covering arrays from large feature models , 2012, SPLC '12.
[49] Giuliano Antoniol,et al. Software project planning for robustness and completion time in the presence of uncertainty using multi objective search based software engineering , 2009, GECCO.
[50] 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.
[51] Myra B. Cohen,et al. Constructing Interaction Test Suites for Highly-Configurable Systems in the Presence of Constraints: A Greedy Approach , 2008, IEEE Transactions on Software Engineering.
[52] Bo Wang,et al. SmartFixer: fixing software configurations based on dynamic priorities , 2013, SPLC '13.
[53] A. Dias-Neto,et al. 0006/2011 - Threats to Validity in Search-based Software Engineering Empirical Studies , 2011 .
[54] 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.
[55] R. K. Ursem. Multi-objective Optimization using Evolutionary Algorithms , 2009 .
[56] Bran Selic,et al. Cyber-physical system product line engineering: comprehensive domain analysis and experience report , 2015, SPLC.
[57] Paolo Arcaini,et al. Quality Indicators in Search-based Software Engineering , 2020, ACM Trans. Softw. Eng. Methodol..
[58] J. Rice. Mathematical Statistics and Data Analysis , 1988 .
[59] Li Zhang,et al. Nonconformity Resolving Recommendations for Product Line Configuration , 2016, 2016 IEEE International Conference on Software Testing, Verification and Validation (ICST).
[60] Shuai Wang,et al. Search-Based Cost-Effective Test Case Selection within a Time Budget: An Empirical Study , 2016, GECCO.
[61] Austen Rainer,et al. Case Study Research in Software Engineering - Guidelines and Examples , 2012 .
[62] Douglas C. Schmidt,et al. Automated Diagnosis of Product-Line Configuration Errors in Feature Models , 2008, 2008 12th International Software Product Line Conference.
[63] 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).
[64] Chunhui Wang,et al. UMTG: a toolset to automatically generate system test cases from use case specifications , 2015, ESEC/SIGSOFT FSE.
[65] David W. Coit,et al. Multi-objective optimization using genetic algorithms: A tutorial , 2006, Reliab. Eng. Syst. Saf..
[66] Jacques Klein,et al. Multi-objective test generation for software product lines , 2013, SPLC '13.
[67] Jason Brownlee,et al. Clever Algorithms: Nature-Inspired Programming Recipes , 2012 .
[68] Gordon Fraser,et al. On Parameter Tuning in Search Based Software Engineering , 2011, SSBSE.
[69] Lothar Thiele,et al. A Tutorial on the Performance Assessment of Stochastic Multiobjective Optimizers , 2006 .
[70] Ana Moreira,et al. A cover-based approach for configuration repair , 2014, SPLC.
[71] David W. Corne,et al. Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy , 2000, Evolutionary Computation.
[72] Paolo Tonella,et al. Symbolic search-based testing , 2011, 2011 26th IEEE/ACM International Conference on Automated Software Engineering (ASE 2011).
[73] Antonio J. Nebro,et al. jMetal: A Java framework for multi-objective optimization , 2011, Adv. Eng. Softw..
[74] Gunter Saake,et al. N-dimensional tensor factorization for self-configuration of software product lines at runtime , 2018, SPLC.
[75] Yuanyuan Zhang,et al. Search based software engineering for software product line engineering: a survey and directions for future work , 2014, SPLC.
[76] 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).
[77] Shaukat Ali,et al. Evaluating Variability Modeling Techniques for Supporting Cyber-Physical System Product Line Engineering , 2016, SAM.
[78] Y Zhang,et al. Multi-Objective Search-based Requirements Selection and Optimisation , 2010 .
[79] Shaukat Ali,et al. Using multi-objective search and machine learning to infer rules constraining product configurations , 2019, Automated Software Engineering.
[80] 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).
[81] Arnaud Gotlieb,et al. Practical minimization of pairwise-covering test configurations using constraint programming , 2016, Inf. Softw. Technol..
[82] Lothar Thiele,et al. Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..
[83] Marc Roubens,et al. Multiple criteria decision making , 1994 .
[84] A. E. Eiben,et al. Parameter tuning for configuring and analyzing evolutionary algorithms , 2011, Swarm Evol. Comput..
[85] Thomas Stützle,et al. Automatic Configuration of Multi-objective Optimizers and Multi-objective Configuration , 2020, High-Performance Simulation-Based Optimization.
[86] Arnaud Gotlieb,et al. Practical pairwise testing for software product lines , 2013, SPLC '13.
[87] Sebastian Oster,et al. Automated Incremental Pairwise Testing of Software Product Lines , 2010, SPLC.
[88] Krzysztof Czarnecki,et al. Where Do Configuration Constraints Stem From? An Extraction Approach and an Empirical Study , 2015, IEEE Transactions on Software Engineering.
[89] Shaukat Ali,et al. Mining cross product line rules with multi-objective search and machine learning , 2017, GECCO.
[90] Shaukat Ali,et al. A framework for automated multi-stage and multi-step product configuration of cyber-physical systems , 2020, Software and Systems Modeling.
[91] Marko Rosenmüller,et al. Automating the Configuration of Multi Software Product Lines , 2010, VaMoS.
[92] Shuai Wang,et al. Empowering Testing Activities with Modeling - Achievements and Insights from Nine Years of Collaboration with Cisco , 2017, MODELSWARD.
[93] Peter J. Fleming,et al. Multiobjective optimization and multiple constraint handling with evolutionary algorithms. II. Application example , 1998, IEEE Trans. Syst. Man Cybern. Part A.
[94] Krzysztof Czarnecki,et al. SMTIBEA: a hybrid multi-objective optimization algorithm for configuring large constrained software product lines , 2019, Software & Systems Modeling.
[95] Shuai Wang,et al. Enhancing Test Case Prioritization in an Industrial Setting with Resource Awareness and Multi-objective Search , 2016, 2016 IEEE/ACM 38th International Conference on Software Engineering Companion (ICSE-C).
[96] Jacques Klein,et al. Towards automated testing and fixing of re-engineered Feature Models , 2013, 2013 35th International Conference on Software Engineering (ICSE).
[97] Yuanyuan Zhang,et al. Search Based Requirements Optimisation: Existing Work and Challenges , 2008, REFSQ.
[98] Mark Harman,et al. Multi-objective Software Effort Estimation , 2016, 2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE).
[99] Patrick M. Reed,et al. Comparing state-of-the-art evolutionary multi-objective algorithms for long-term groundwater monitoring design , 2005 .
[100] H. B. Mann,et al. On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other , 1947 .
[101] Phil McMinn,et al. Search‐based software test data generation: a survey , 2004, Softw. Test. Verification Reliab..
[102] Mark Harman,et al. Search Algorithms for Regression Test Case Prioritization , 2007, IEEE Transactions on Software Engineering.
[103] YueTao,et al. Quality Indicators in Search-based Software Engineering , 2020 .