Characteristic for embedded systems is that they have to meet a multitude of quantitative constraints.These constraints involve the resources that a system may use (computation resources, power consumption, memory usage, communication bandwidth, costs, etc.), assumptions about the environment in which it operates (arrival rates, hybrid behaviour), and requirements on the services that the system has to provide (timing constraints, QoS, availability, fault tolerance, etc.). Model-Driven Development (MDD) is a new software development technique in which the primary software artifacts are models providing a collection of views. Existing MDD tools for real-time embedded systems are rather sophisticated in handling functional requirements but their treatment of quantitative constraints is still very limited. Hence MDD will not realise its full potential in the embedded systems area unless the ability to handle quantitative properties is drastically improved.The objective of Quasimodo is to develop theory and techniques for handling quantitative (e.g.real-time, hybrid and stochastic) constraints in model-driven development of real-time embedded systems. More specifically, the project aims at:1. Improving the modeling of diverse quantitative aspects of embedded systems.2. Providing a wide range of powerful techniques for analysing models with quantitative information and for establishing abstraction relations between them.3. Generating predictable code from quantitative models.4. Improving the overall quality of testing by using suitable quantitative models as the basis for automatically generating sound and correct test cases.In order to demonstrate the usefulness of our techniques, we will apply them to several complex industrial case studies, and provide tool components that support quantitative modeling, analysis, implementation and testing of embedded systems.