Automated Strong Mutation Testing of XACML Policies

While the existing methods for testing XACML policies have varying levels of effectiveness, none of them can reveal the majority of policy faults. The undisclosed faults may lead to unauthorized access and denial of service. This paper presents an approach to strong mutation testing of XACML policies that automatically generates tests from the mutants of a given policy. Such mutants represent the targeted faults that may appear in the policy. In this approach, we first compose the strong mutation constraints that capture the semantic difference between each mutant and its original policy. Then, we use a constraint solver to derive an access request (i.e., test). The test suite generated from all the mutants of a policy can achieve a perfect mutation score, thus uncover all hypothesized faults or demonstrate their absence. Based on the mutation-based approach, this paper further explores optimal test suite that achieves a perfect mutation score without duplicate tests. To evaluate the proposed approach, our experiments have included all the subject policies in the relevant literature and used a number of new policies. The results demonstrate that: (1) it is scalable to generate a mutation-based test suite to achieve a perfect mutation score, (2) it can be impractical to generate the optimal test suite due to the expensive removal of duplicate tests, (3) different from the results of the existing study, the modified-condition/decision coverage-based method, currently the most effective one, has low mutation scores for several policies.