Generation of adequate test cases is difficult and expensive, especially for testing software systems whose input is structurally complex. This paper presents an approach called data mutation to generating a large number of test data from a few seed test cases. It is inspired by mutation testing methods, but differs from them in the aim and the way that mutation operators are defined and used. While mutation testing is a method for measuring test adequacy, data mutation is a method of test case generation. In traditional mutation testing, mutation operators are used to transform the program under test. In contrast, mutation operators in our approach are applied on input data to generate test cases, hence called data mutation operators. The paper reports a case study with the method on testing an automated modelling tool to illustrate the applicability of the proposed method. Experiment data clearly demonstrate that the method is adequate and cost effective, and able to detect a large proportion of faults.

Modelling tools play a crucial role in model-driven software development methods. A particular difficulty in testing such software systems is the generation of adequate test cases because the test data are structurally complicated. This paper proposes an approach called data mutation to generating a large number of test data from a few seed test cases. It is inspired in mutation testing methods, but differs from them in the way that mutation operators are defined and used. In our approach, mutation operators transform the input data rather than the program under test or the specification of the software. It is not a test adequacy measurement. Instead, it generates test cases. The paper also reports a case study with the method on testing a modelling tool and illustrates the applicability of the proposed method. Experiment data clearly demonstrated that the method can achieve a high test adequacy. It has a high fault detecting ability and good cost effectiveness.