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An empirical evaluation of evolutionary algorithms for unit test suite generation
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Seeding strategies in search‐based unit test generation
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Random or evolutionary search for object‐oriented test suite generation?
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Search‐based software test data generation: a survey
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A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimization: NSGA-II
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Cellular Genetic Algorithms
inferredcites →https://doi.org/10.1007/978-3-319-22183-0_7openalex-snapshot
Combining Multiple Coverage Criteria in Search-Based Unit Test Generation
inferredcites →https://doi.org/10.1007/978-3-319-47106-8_5openalex-snapshot
Search-Based Testing of Procedural Programs: Iterative Single-Target or Multi-target Approach?
inferredcites →https://doi.org/10.1007/978-3-319-66299-2_1openalex-snapshot
Many Independent Objective (MIO) Algorithm for Test Suite Generation
inferredcites →https://doi.org/10.1007/978-3-319-66299-2_3openalex-snapshot
An Empirical Evaluation of Evolutionary Algorithms for Test Suite Generation
inferredcites →https://doi.org/10.1007/978-3-319-66299-2_6openalex-snapshot
LIPS vs MOSA: A Replicated Empirical Study on Automated Test Case Generation
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Simulationsmethoden in der Medizin und Biologie
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Data Engineering and Intelligent Computing
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A Comprehensive Comparison of Ant Colony and Hybrid Particle Swarm Optimization Algorithms Through Test Case Selection
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Analysing the fitness landscape of search-based software testing problems
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Parameter tuning or default values? An empirical investigation in search-based software engineering
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A comparative study of many-objective evolutionary algorithms for the discovery of software architectures
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A detailed investigation of the effectiveness of whole test suite generation
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Chemical Reaction Optimization: a tutorial
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Random search techniques for optimization problems
inferredcites →https://doi.org/10.1016/j.asoc.2016.09.045openalex-snapshot
Comparisons of metaheuristic algorithms and fitness functions on software test data generation
inferredcites →https://doi.org/10.1016/j.infsof.2018.05.003openalex-snapshot
Test suite generation with the Many Independent Objective (MIO) algorithm
inferredcites →https://doi.org/10.1016/j.tcs.2014.11.028openalex-snapshot
From black-box complexity to designing new genetic algorithms
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Evolutionary test environment for automatic structural testing
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Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces
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A farewell to Bonferroni: the problems of low statistical power and publication bias
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No free lunch theorems for optimization
inferredcites →https://doi.org/10.1109/4235.996017openalex-snapshot
A fast and elitist multiobjective genetic algorithm: NSGA-II
inferredcites →https://doi.org/10.1109/ccaa.2016.7813848openalex-snapshot
A differential evolution based approach to generate test data for data-flow coverage
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Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)
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The Pareto archived evolution strategy: a new baseline algorithm for Pareto multiobjective optimisation
inferredcites →https://doi.org/10.1109/cec.2009.4983313openalex-snapshot
Comparing algorithms for search-based test data generation of Matlab® Simulink® models
inferredcites →https://doi.org/10.1109/cec.2013.6557623openalex-snapshot
A comparison of evolutionary algorithms on a set of antenna design benchmarks
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Comparative analysis of classical multi-objective evolutionary algorithms and seeding strategies for pairwise testing of Software Product Lines
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A comparative study of evolutionary algorithms for phase shifting transformer setting optimization
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It Does Matter How You Normalise the Branch Distance in Search Based Software Testing
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Reformulating Branch Coverage as a Many-Objective Optimization Problem
inferredcites →https://doi.org/10.1109/icst.2017.19openalex-snapshot
Private API Access and Functional Mocking in Automated Unit Test Generation
inferredcites →https://doi.org/10.1109/qsic.2011.19openalex-snapshot
Evolutionary Generation of Whole Test Suites
inferredcites →https://doi.org/10.1109/tevc.2009.2033580openalex-snapshot
Chemical-Reaction-Inspired Metaheuristic for Optimization
inferredcites →https://doi.org/10.1109/tse.2017.2663435openalex-snapshot
Automated Test Case Generation as a Many-Objective Optimisation Problem with Dynamic Selection of the Targets
inferredcites →https://doi.org/10.1126/science.220.4598.671openalex-snapshot
Optimization by Simulated Annealing
inferredcites →https://doi.org/10.1136/bmj.316.7139.1236openalex-snapshot
What's wrong with Bonferroni adjustments
inferredcites →https://doi.org/10.1145/1146238openalex-snapshot
Proceedings of the 2006 international symposium on Software testing and analysis
inferredcites →https://doi.org/10.1145/1273463.1273475openalex-snapshot
A theoretical & empirical analysis of evolutionary testing and hill climbing for structural test data generation
inferredcites →https://doi.org/10.1145/2025113openalex-snapshot
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
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Comparing search techniques for finding subtle higher order mutants
inferredcites →https://doi.org/10.1145/2642937openalex-snapshot
Proceedings of the 29th ACM/IEEE International Conference on Automated Software Engineering
inferredcites →https://doi.org/10.1145/2642937.2642986openalex-snapshot
Automated unit test generation for classes with environment dependencies
inferredcites →https://doi.org/10.1145/2685612openalex-snapshot
A Large-Scale Evaluation of Automated Unit Test Generation Using EvoSuite
inferredcites →https://doi.org/10.1145/2739480.2754696openalex-snapshot
Random or Genetic Algorithm Search for Object-Oriented Test Suite Generation?
inferredcites →https://doi.org/10.1145/2786805openalex-snapshot
Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering
inferredcites →https://doi.org/10.1145/2786805.2786838openalex-snapshot
Modeling readability to improve unit tests
inferredcites →https://doi.org/10.1155/2014/739768openalex-snapshot
The Coral Reefs Optimization Algorithm: A Novel Metaheuristic for Efficiently Solving Optimization Problems
inferredcites →https://doi.org/10.1162/106365600568202openalex-snapshot
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
inferredcites →https://doi.org/10.1162/106365605774666921openalex-snapshot
On the Choice of the Offspring Population Size in Evolutionary Algorithms
inferredcites →https://doi.org/10.1162/evco.1993.1.1.25openalex-snapshot
Predictive Models for the Breeder Genetic Algorithm I. Continuous Parameter Optimization
inferredcites →https://doi.org/10.1609/aaai.v25i1.8037openalex-snapshot
Convergence Properties of (μ + λ) Evolutionary Algorithms
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