Abstract:
As the technologies of autonomic computing become more prevalent, it is essential to develop methodologies for testing their dynamic self-management operations. Self-management features in autonomic systems induce structural and behavioral changes to the system during its execution, which should be validated to avoid costly system failures. The high-level of automation in autonomic systems also means that human errors such as incorrect goal specification could yield potentially disastrous effects on the components being managed; further emphasizing the need for runtime testing.

In this talk, I present a self-testing framework for autonomic computing systems to dynamically validate change requests. The framework extends the current architecture of autonomic systems to include self-testing as an implicit characteristic, regardless of the specific self-management features being implemented. The testing approach is based on two general strategies: (1) Safe Adaptation with Validation - performs testing directly on managed resources during safe adaptation, and (2) Replication with Validation - performs testing on a copy of the managed resource. I will also describe the details of a prototype developed to show the feasibility of the testing approach.

Biography:
Tariq holds a Bachelor's degree in Computer Science from the Florida Institute of Technology, and a Master's degree in Computer Science from Florida International University (FIU) where he is currently a doctoral candidate. His research area is software engineering with strong emphasis on automated software testing and verification techniques for autonomic systems, model-driven software development, and model-based testing. Tariq is a member of the Software Testing Research Group at FIU.