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Cars, trains, trucks, telecom networks and industrial robots are examples of products relying on complex embedded software systems, running on embedded computers. Such systems may consist of millions of lines of program code developed by hundreds of engineers over many years, often decades.

Over the long life-cycle of such systems, the main part of the product development costs is typically not the initial development, but the software maintenance, i.e., improvements and corrections of defects, over the years. Of the maintenance costs, a major cost is the verification of the system after changes has been applied, which often requires a huge amount of testing. However, today's techniques are not sufficient, as defects often are found post-release, by the customers. This area is therefore of high relevance for industry.

Complex embedded systems often control machinery where timing is crucial for accuracy and safety. Such systems therefore have important requirements on timing, such as maximum response times. However, when maintaining complex embedded software systems, it is difficult to predict how changes may impact the system's run-time behavior and timing, e.g., response times.Analytical and formal methods for timing analysis exist, but are often hard to apply in practice on complex embedded systems, for several reasons. As a result, the industrial practice in deciding the suitability of a proposed change, with respect to its run-time impact, is to rely on the subjective judgment of experienced developers and architects. This is a risky and inefficient, trial-and-error approach, which may waste large amounts of person-hours on implementing unsuitable software designs, with potential timing- or performance problems. This can generally not be detected at all until late stages of testing, when the updated software system can be tested on system level, under realistic conditions. Even then, it is easy to miss such problems. If products are released containing software with latent timing errors, it may cause huge costs, such as car recalls, or even accidents. Even when such problems are found using testing, they necessitate design changes late in the development project, which cause delays and increases the costs.

This thesis presents an approach for impact analysis with respect to run-time behavior such as timing and performance for complex embedded systems. The impact analysis is performed through optimizing simulation, where the simulation models are automatically generated from the system implementation. This approach allows for predicting the consequences of proposed designs, for new or modified features, by prototyping the change in the simulation model on a high level of abstraction, e.g., by increasing the execution time for a particular task. Thereby, designs leading to timing-, performance-, or resource usage problems can be identified early, before implementation, and a late redesigns are thereby avoided, which improves development efficiency and predictability, as well as software quality.

The contributions presented in this thesis is within four areas related to simulation-based analysis of complex embedded systems: (1) simulation and simulation optimization techniques, (2) automated model extraction of simulation models from source code, (3) methods for validation of such simulation models and (4) run-time recording techniques for model extraction, impact analysis and model validation purposes. Several tools has been developed during this work, of which two are in commercialization in the spin-off company Percepio AB. Note that the Katana approach, in area (2), is subject for a recent patent application - patent pending.