The current practice to design software for real-time systems is tedious. There is almost no tool support that assists the designer in automatically deriving safe bounds of the worst-case execution time (WCET) of a system during code generation and in systematically optimizing code to reduce WCET.This article presents concepts and infrastructures for WCET-aware code generation and optimization techniques for WCET reduction. All together, they help to obtain code explicitly optimized for its worst-case timing, to automate large parts of the real-time software design flow, and to reduce costs of a real-time system by allowing to use tailored hardware.

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SUMMARY With the growing complexity of embedded systems software, high code quality can only be achieved using a compiler. Sophisticated compilers provide a vast spectrum of various optimizations to improve code aggressively w.r.t. different objective functions, e.g. average‐case execution time (ACET) or code size. Owing to the complex interactions between the optimizations, the choice for a promising sequence of code transformations is not trivial. Compiler developers address this problem by proposing standard optimization levels, e.g. O3 or Os. However, previous studies have shown that these standard levels often miss optimization potential or might even result in performance degradation. In this paper, we propose the first adaptive worst‐case execution time (WCET)‐aware compiler framework for an automatic search of compiler optimization sequences that yield highly optimized code. Besides the objective functions ACET and code size, we consider the WCET which is a crucial parameter for real‐time systems. To find suitable trade‐offs between these objectives, stochastic evolutionary multi‐objective algorithms identifying Pareto optimal solutions for the objectives 〈WCET, ACET 〉 and 〈WCET, code size 〉 are exploited. A comparison based on statistical performance assessments is performed that helps to determine the most suitable multi‐objective optimizer. The effectiveness of our approach is demonstrated on real‐life benchmarks showing that standard optimization levels can be significantly outperformed. Copyright © 2011 John Wiley & Sons, Ltd.

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