In access control systems, aimed at regulating the accesses to protected data and resources, a critical component is the Policy Decision Point (PDP), which grants or denies the access according to the defined policies. Due to the complexity of the standard languag-e, it is recommended to rely on model-driven approaches which allow to overcome difficulties in the XACML policy definition. We provide in this paper a toolchain that involves a model-driven approach to specify and generate XACML policies and also enables automated testing of the PDP component. We use XACML-based testing strategies for generating appropriate test cases which are able to validate the functional aspects, constraints, permissions and prohibitions of the PDP. An experimental assessment of the toolchain and its use on a realistic case study are also presented.

As security requirements of software often change, developers may modify security policies such as access control policies (policies in short) according to evolving requirements. To increase confidence that the modification of policies is correct, developers conduct regression testing. However, rerunning all of existing system test cases could be costly and time-consuming. To address this issue, we develop a regression-test-selection approach, which selects every system test case that may reveal regression faults caused by policy changes. Our evaluation results show that our test-selection approach reduces a substantial number of system test cases efficiently.

In order to facilitate managing authorization, access control architectures are designed to separate the business logic from an access control policy. To determine whether a user can access which resources, a request is formulated from a component, called a Policy Enforcement Point (PEP) located in application code. Given a request, a Policy Decision Point (PDP) evaluates the request against an access control policy and returns its access decision (i.e., permit or deny) to the PEP. With the growth of sensitive information for protection in an application, an access control policy consists of a larger number of rules, which often cause a performance bottleneck. To address this issue, we propose to refactor access control policies for performance improvement by splitting a policy (handled by a single PDP) into its corresponding multiple policies with a smaller number of rules (handled by multiple PDPs). We define seven attribute-set-based splitting criteria to facilitate splitting a policy. We have conducted an evaluation on three subjects of real-life Java systems, each of which interacts with access control policies. Our evaluation results show that (1) our approach preserves the initial architectural model in terms of interaction between the business logic and its corresponding rules in a policy, and (2) our approach enables to substantially reduce request evaluation time for most splitting criteria.