Generation of algebraic data type values using evolutionary algorithms

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@Article{Ballesteros:2025:jlamp,

• author = "Ignacio Ballesteros and Clara Benac-Earle and Julio Marino and Lars-Ake Fredlund and Angel Herranz",
• title = "Generation of algebraic data type values using evolutionary algorithms",
• journal = "Journal of Logical and Algebraic Methods in Programming",
• year = "2025",
• volume = "143",
• pages = "101022",
• keywords = "genetic algorithms, genetic programming, Evolutionary algorithms, Software testing, Property-based testing, Red-black tree",
• ISSN = "2352-2208",
• URL = "https://www.sciencedirect.com/science/article/pii/S2352220824000762",
• DOI = "doi:10.1016/j.jlamp.2024.101022",
• abstract = "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. First, the approach is illustrated with the generation of sorted lists. Then, we generalise the technique to arbitrary algebraic data type definitions. Finally, we consider the problem of constrained data types where the data must satisfy some nontrivial property, using the well-known example of red-black trees for our experiments. 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. Our experiments have revealed that this evolutionary approach is able to improve diversity, and increase the size of valid generated values with respect to simple random sampling techniques",

}

Genetic Programming entries for Ignacio Ballesteros Clara Benac-Earle Julio Marino Lars-Ake Fredlund Angel Herranz

Citations