Generating Test Cases from Role-Based Access Control Policies using Cause-Effect Graph

Abstract

Role-based access control is one of the fundamental security models used to ensure the confidentiality and integrity of information by specifying policies and enforcing them through mechanisms. Usually, authorization constraints are defined on policies to enforce some regulations such as a user cannot be assigned to two conflicting roles. Once the RBAC mechanisms are implemented in a system, testing is performed to ensure the correctness of the implementation. Black-box testing is one approach for software testing where test cases are generated from the specification. The challenge of this approach is the huge number of test cases that can be generated.This paper aims at reducing the number of test cases required to test the implementation of RBAC system. To achieve that, we use a cause-effect graph to specify policies, and then link authorization constraints to the cause-effect graph constraints. The specification of constraints within the cause-effect graph allows reducing the number of test cases by removing the useless cases due to authorization constraints. We illustrate our technique through an illustrative example with the aid of the BenderRBT tool. The results show that the number of test cases is significantly reduced.

Usage of Machine Learning Methods for Cause-Effect Graph Feasibility Prediction

“…Cause-effect graphs (CEGs) [4] are a black-box testing method which is often applied on complex, industrial, safety-critical or real-time systems (e.g. access control policies [5], high-speed trains [6], and quantum programming [7]) where the usage of other methods would be very time-consuming or impossible due to different limitations. Creating a CEG specification does not require programming knowledge or familiarity with software quality metrics.…”