With technologies increasing rapidly, symbolic, quantitative modeling and computer-based simulation (M&S) have become affordable and easy-to-apply tools in numerous application areas as, e.g., supply chain management, pilot training, car safety improvement, design of industrial buildings, or theater-level war gaming. M&S help to reduce the resources required for many types of projects, accelerate the development of technical systems, and enable the control and management of systems of high complexity. However, as the impact of M&S on the real world grows, the danger of adverse effects of erroneous or unsuitable models or simu-lation results also increases. These effects may range from the delayed delivery of an item ordered by mail to hundreds of avoidable casualties caused by the simulation-based acquisi-tion (SBA) of a malfunctioning communication system for rescue teams. In order to benefit from advancing M&S, countermeasures against M&S disadvantages and drawbacks must be taken. Verification and Validation (V&V) of models and simulation results are intended to ensure that only correct and suitable models and simulation results are used. However, during the development of any technical system including models for simulation, numerous errors may occur. The later they are detected, and the further they have propagated through the model development process, the more resources they require to correct thus, their propaga-tion should be avoided. If the errors remain undetected, and major decisions are based on in-correct or unsuitable models or simulation results, no benefit is gained from M&S, but a dis-advantage. This thesis proposes a structured and rigorous approach to support the verification and valida-tion of models and simulation results by a) the identification of the most significant of the current deficiencies of model develop-ment (design and implementation) and use, including the need for more meaningful model documentation and the lack of quality assurance (QA) as an integral part of the model development process; b) giving an overview of current quality assurance measures in M&S and in related areas. The transferability of concepts like the capability maturity model for software (SW-CMM) and the ISO9000 standard is discussed, and potentials and limits of documents such as the VV&A Recommended Practices Guide of the US Defense Modeling and Simulation Office are identified; c) analysis of quality assurance measures and so called V&V techniques for similarities and differences, to amplify their strengths and to reduce their weaknesses. d) identification and discussion of influences that drive the required rigor and intensity of V&V measures (risk involved in using models and simulation results) on the one hand, and that limit the maximum reliability of V&V activities (knowledge about both the real system and the model) on the other. This finally leads to the specification of a generalized V&V process - the V&V Triangle. It illustrates the dependencies between numerous V&V objectives, which are derived from spe-cific potential errors that occur during model development, and provides guidance for achiev-ing these objectives by the association of V&V techniques, required input, and evidence made available. The V&V Triangle is applied to an M&S sample project, and the lessons learned from evaluating the results lead to the formulation of future research objectives in M&S V&V.