An automated approach to monitoring and diagnosing requirements

Monitoring the satisfaction of software requirements and diagnosing what went wrong in case of failure is a hard problem that has received little attention in the Software and Requirement Engineering literature. To address this problem, we propose a framework adapted from artificial intelligence theories of action and diagnosis. Specifically, the framework monitors the satisfaction of software requirements and generates log data at a level of granularity that can be tuned adaptively at runtime depending on monitored feedback. When errors are found, the framework diagnoses the denial of the requirements and identifies problematic components. To support diagnostic reasoning, we transform the diagnostic problem into apropositional satisfiability (SAT) problem that can be solved by existing SAT solvers. We preprocess log data into a compact propositional encoding that better scales with problem size. The proposed theoretical framework has been implemented as a diagnosing component that will return sound and complete diagnoses accounting for observed aberrant system behaviors. Our solution is illustrated with two medium-sized publicly available case studies: a Web-based email client and an ATM simulation. Our experimental results demonstrate the feasibility of scaling our approach to medium-size software systems