Abstract: Today, software is found in almost all systems, vehicles, communication devices, medical equipments, and entertainment, for example. The size and complexity of these systems has grown continuously over the last forty years---the time span for modern competing. The latest release of the Windows operating system, called Vista, is expected to he more than fifty million lines of code, about 4% bigger than the previous version. Some of the reasons for this are numerous feature demands and the neck to support multiple platforms, and need for compatibility with legacy software and hardware. Each line of code, in these large systems, requires perhaps several technical decisions, often, but not simple. The sheer volume of this decision making process is daunting. No single human can fully understand a system of high complexity. To help ameliorate this problem, systems are decomposed into subsystems, libraries, modules, and classes. Most of these components have interdependencies, in order to provide services, one to another. However, in systems of great size, the dependencies often become a dense web of relationships. It is exactly this problem on which we focus in this research. We propose that static dependency structure is an important element to understand the state of large software system. We conduct various analyses using well-known existing open source, commercial and expert developed projects, including our own projects to evaluate the overall effectiveness of our approaches. We detect structural problems in large software development projects, and present a structure metric to rank software tiles according to their risk contribution to the software system. Additionally, we present a model that indexes software components according to their potential for reuse. We design and conduct experiment to investigate the impact of change in one file on other files. Furthermore, we provide tools needed to support analysis, project visualization and monitoring. Finally, the investigate corrective procedures and simulate their application, monitoring improvements in observed defects.