No description available.

No description available.

No description available.

No description available.

Automatic test case generation is a process that starts with text based functional requirements which are converted to a formal system requirements model. Once the formal system requirements model is created the automatic test case generation software creates a set of test scenarios that will verify that the requirements are all met. The automatic test case generation software accomplishes the conversion in a four step process: create base scenarios, identify unverified requirements, enhance scenarios to cover all requirements and allow black box testing, and then combine the scenarios into a single scenario tree. The automatic test case generation system outputs a set of scenarios by walking the final scenario tree. This dissertation expands on automatic test case generation for embedded systems in two major ways. The first is to extend functional automatic test case generation to allow for time based requirements as first class objects. The second is to use the automatic test case generation system to enable system partitioning decisions. The addition of time based requirements to the automatic test case generation system allows more complex systems to be developed. By providing a partitioning recommendation based on the test cases generated from the system requirements, the scope and capabilities of a single designer can be expanded to more complex systems. The resulting upgrades to the theory of automatic test case generation could be applied to the existing tools or incorporated in modern UML/SysML based design tools.

This dissertation provides several techniques for solving a class of nonconvex optimization problems that arise in the thermal analysis of electronic chip packages. The topic is of interest because in systems containing delicate electronic components both performance and reliability are impacted by thermal behavior. A modeling paradigm, called Compact Thermal Modeling (CTM), has been demonstrated to show promise for accurately estimating steady state thermal behavior without resorting to computationally intensive finite element models or expensive direct experimentation. The CTM is a network model that gives rise to a nonconvex optimization problem. A solution to this nonconvex optimization problem provides a reasonably accurate characterization of the steady state temperature profile the chip will attain under arbitrary boundary conditions, which allows the system designer to model the application of a wide range of thermal design strategies with useful accuracy at reasonable computational cost. This thesis explores several approaches to solving the optimization problem. We present a heuristic technique that is an adaptation of the classical coordinate search method that has been adapted to run efficiently by exploiting the algebraic structure of the problem. Further, the heuristic is able to avoid stalling in poor local optima by using a partitioning scheme that follows from an examination of special structure in the problem's feasible region. We next present several exact approaches using a globally optimal method based on the Reformulation Linearization Technique (RLT). This approach generates and then solves convex relaxations of the original problem, tightening the approximations within a branch and bound framework. We then explore several approaches to improving the performance of the RLT technique by introducing variable substitutions and valid inequalities, which tighten the convex relaxations. Computational results, conclusions, and recommendations for further research are also provided.

No description available.

No description available.

No description available.

No description available.