1. 1 Toward a Modeling Theory for Predictable Complex Software Designs by Levent Yilmaz Auburn Modeling and Simulation Laboratory Department of Computer Science & Engineering Auburn University Auburn, AL 36849

2. 2  Predictability: Toward a Modeling Theory for Predictable Complex System Designs  Off-Line Prediction with Model-Based Reasoning  On-Line Prediction: Exploratory Multisimulation of Alternative Designs with On-line Model Recommenders

3. 3 Predicting the Behavior of “Possible” Systems using Simulation Modeling Motivation: Large complex systems constantly evolve and their maintenance involves predicting and reasoning about the behavioral impacts of changes (i.e., substitutions) and alternative design organizations. –The Need: Need to compare design alternatives by analyzing the options of many possible systems, for which simulations may or may not yet have been built; hence may not be empirically studied. Problem: Can simulation modeling excite an emerging prospect for developing a software design theory that supports off-line, as well as on-line exploration and reasoning about the behavior of “possible” systems? Proposed Strategy: A hybrid approach that combines model-based reasoning with dynamic exploratory multisimulations to facilitate “effective” analysis for predictable design evolution.

4. 4 Off-Line Prediction: Modular Reasoning about Complex Software-Intensive Systems Designing systems as assemblies of standard component models whose behaviors are already understood in isolation. System’s behavior must be understood using the component models and their local interconnections. Approach: –Describe the behavior of each simulation model component in isolation using a mathematical model. –Use the connections among components that depict the composition style and mathematical models of model components to derive a mathematical model of the behavior of the system as a whole. –Enforce the property that mathematical model of the behavior of the system involves modular composition of the mathematical models of components.

5. 5 Component-Connector Approaches At best an ad-hoc, non-uniform framework for composition rule and behavioral reasoning. Computed properties are features of the global system. No leverage for “effective” modular reasoning.

6. 6 A Different Taxonomy: Reexamining the System Entity-Structure/MB Framework P2P2 P1P1 P2P2 AABA B A B P1P1 AABA B Develop a mathematical model that captures system properties of interest. Determine components (operands) and operators. Syntax rule: Bind actual parameters to formal parameters. Behavioral rule: Templates are functions applied to mathematical model of components. AA P1P1 P1P1 P1P1 P1P1 P1P1 P1P1

7. 7 Parametric Simulation with DEVS Templates L AA S S B S A B A2B1A1A3 L S S S S1 LL1 L2 Simulation Templates Concrete components B2 simulates Abstract Template Abstract Component simulates

8. 8 On-Line Prediction: Multisimulation of Alternative Design Configurations with Model Recommenders Multisimulation is dynamic, exploratory, and simultaneous experimentation with alternative design configurations. Given that complex, dynamically reconfigurable, distributed systems that operate in unpredictable context are common in today’s component-based mission-critical systems, Multisimulation with model recommenders can be useful in at least two ways: Design Time: Simultaneous run-time exploration of the utility and effectiveness of alternative design decisions (i.e., configurations) under emerging conditions. After Deployment: Symbiotic simulation to support making recommendations on possible system configurations that help achieve or optimize quality objectives of interest.

9. 9 Thank you