The task of elaborating accurate test suites for program
testing can be an extensive computational work. Mutation
testing is not immune to the problem of being a computational and time-consuming task so that it has found relief in the use of heuristic techniques. The use of Genetic Algorithms in mutation
testing has proved to be useful for probing test suites, but it has
mainly been enclosed only in the field of imperative programming
paradigms. Therefore, we decided to test the feasibility of using
Genetic Algorithms for performing mutation testing in functional
programming environments. We tested our proposal by making a
graph representations of four different functional programs and
applied a Genetic Algorithm to generate a population of mutant
programs. We found that it is possible to obtain a set of mutants
that could find flaws in test suites in functional programming
languages. Additionally, we encountered that when a source code
increases its number of instructions it was simpler for a genetic
algorithm to find a mutant that can avoid all of the test cases
