A testability transformation is a source-to-source transformation that aims to improve the ability of a given test generation method to generate test data for the original program. We introduce testability transformation, demonstrating that it differs from traditional transformation, both theoretically and practically, while still allowing many traditional transformation rules to be applied. We illustrate the theory of testability transformation with an example application to evolutionary testing. An algorithm for flag removal is defined and results are presented from an empirical study which show how the algorithm improves both the performance of evolutionary test data generation and the adequacy level of the test data so-generated.

This paper reports the results of a study on the impact of a type of side effect (SE) upon program comprehension. We applied a crossover design on different tests involving fragments of C code that include increment and decrement operators. Each test had an SE version and a side-effect-free counterpart. The variables measured in the treatments were the number of correct answers and the time spent in answering. The results show that the side-effect operators considered significantly reduce performance in comprehension-related tasks, providing empirical justification for the belief that side effects are harmful.

Evolutionary testing is a search based approach to the automated generation of systematic test data, in which the search is guided by the test data adequacy criterion. Two problems for evolutionary testing are the large size of the search space and structural impediments in the implementation of the program which inhibit the formulation of a suitable fitness function to guide the search. In this paper we claim that slicing can be used to narrow the search space and transformation can be applied to the problem of structural impediments. The paper presents examples of how these two techniques have been successfully employed to make evolutionary testing both more efficient and more effective.

Side-effects are widely believed to impede program comprehension and have a detrimental effect upon software maintenance. This paper introduces an algorithm for side-effect removal which splits the side-effects into their pure expression meaning and their state-changing meaning. Symbolic execution is used to determine the expression meaning, while transformation is used to place the state-changing part in a suitable location in a transformed version of the program. This creates a program which is semantically equivalent to the original but guaranteed to be free from side-effects. The paper also reports the results of an empirical study which demonstrates that the application of the algorithm causes a significant improvement in program comprehension.