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Security is a key-challenge for software engineering, especially when considering access control and software evolutions. No satisfying solution exists for maintaining the alignment of access control policies with the business logic. Current implementations of access control rely on the separation between the policy and the application code. In practice, this separation is not so strict and some rules are hardcoded within the application, making the evolution of the policy difficult. We propose a new methodology for implementing security-driven applications. From a policy defined by a security expert, we generate an architectural model, reflecting the access control policy. We leverage the advances in the models@runtime domain to keep this model synchronized with the running system. When the policy is updated, the architectural model is updated, which in turn reconfigures the running system. As a proof of concept, we apply the approach to the development of a library management system.

Abstract

Access control policies in software systems can be implemented incorrectly for various reasons. This paper presents a model-based approach for automated testing of access control implementation. To feed the model-based testing process, test models are constructed by integrating declarative access control rules and contracts (preconditions and post-conditions) of the associated activities. The access control tests are generated from the test models to exercise the interactions of access control activities. Test executability is obtained through a mapping of the modeling elements to implementation constructs. The approach has been implemented in an industry-adopted test automation framework that supports the generation of test code in a variety of languages, such as Java, C, C++, C#, and HTML/Selenium IDE. The full model-based testing process has been applied to two systems implemented in Java. The effectiveness is evaluated in terms of access-control fault detection rate using mutation analysis of access control implementation. The experiments show that the model-based tests killed 99.7% of the mutants and the remaining mutants caused no policy violations.