Compiler-based optimizations are efficient techniques to improve a program to be compiled. Function inlining is a well-known compiler-based optimization that substitutes function calls by the body of the function. The data represent the results of multiobjective function inlining for hard real-time systems with code size, energy consumption and worst-case execution time (WCET) as objectives. Since the analyses of energy consumption and WCET are very time-consuming at compile time, search space reduction and predictions based on machine learning techniques were applied to speed up the multiobjective function inlining at compile time.
