Work in Progress: Generation of Algebraic Data Types using Evolutionary Algorithms





Publicado en

Actas de las XXII Jornadas sobre Programación y Lenguajes (PROLE 2023)

Licencia Creative Commons


Automatic data generation is a key component of automated software testing. Random generation of test input data can uncover some bugs in software, but its effectiveness decreases when those inputs must satisfy complex properties in order to be meaningful. In this work we study an evolutionary approach to generate values that can be encoded as algebraic data types plus additional properties. More specifically, we have conducted an experiment on the automated generation of red-black trees using evolutionary algorithms. Although relatively simple, this example will allow us to introduce the main principles of evolutionary algorithms and how these principles can be applied to obtain valid, nontrivial samples of a given data structure. While the preliminary results show some potential, further experimentation is needed.


Acerca de Ballesteros, Ignacio

Palabras clave

Evolutionary Algorithms, Genetic Programming, Software Testing, Property-based Testing, Red-black Trees
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