Selection and Evaluation of Test Data Sets Based on Genetic Programming

Abstract

A testing criterion is a predicate to be satisfied and generally addresses two important questions related to: 1) the selection of test cases capable of revealing as many faults as possible; and 2) the evaluation of a test set to consider the test ended. Studies show that fault based criteria, such as mutation testing, are very efficacious, but very expensive in terms of the number of test cases. Mutation testing uses mutation operators to generate alternatives for the program P under test. The goal is to derive test cases to producing different behaviours in P and its alternatives. This approach usually does not allow the test of interaction between faults since the alternative differs from P by a simple modification. This work explores the use of Genetic Programming (GP) to derive alternatives for testing P and describes two GP-based test procedures for selection and evaluation of test data. Experimental results show the GP approach applicability and allow comparison with mutation testing.