A Simple and Practical Approach to Unit Testing: The JML and JUnit Way
ECOOP '02 Proceedings of the 16th European Conference on Object-Oriented Programming
Fault model-driven test derivation from finite state models: annotated bibliography
Modeling and verification of parallel processes
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
Prioritizing JUnit Test Cases: An Empirical Assessment and Cost-Benefits Analysis
Empirical Software Engineering
Representations for Genetic and Evolutionary Algorithms
Representations for Genetic and Evolutionary Algorithms
Weak Mutation Testing and Completeness of Test Sets
IEEE Transactions on Software Engineering
Finding Minimum Spanning/Distances Trees by Using River Formation Dynamics
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
Applying Evolutionary Computation Methods to Formal Testing and Model Checking
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
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Selecting an appropriate test suite to detect faults in a program is a difficult task. In the case of functional languages, altough there are some additional difficulties (due to the lack of state, laziness and its higher-order nature), we can also take advantage of higher-order programming to allow defining generic ways of obtaining tests. In this paper we present a genetic algorithm to automatically select appropriate criteria to generate tests for Haskell programs. As a case study, we apply the algorithm to deal with a program using red-black trees.