  Empirical software engineering is concerned with the design and analysis of
empirical studies that include software products, processes, and resources.
Optimization is a form of data analytics in support of human decision-making.
Optimization methods are aimed to find the best decision alternatives.
Empirical studies serve both as a model and as data input for optimization. In
addition, the complexity of the models used for optimization trigger further
studies on explaining and validating the results in real-world scenarios. The
goal of this chapter is to give an overview of the as-is and of the to-be usage
of optimization in software engineering. The emphasis is on pragmatic use of
optimization, and not so much on describing the most recent algorithmic
innovations and tool developments. The usage of optimization covers a wide
range of questions from different types of software engineering problems along
the whole life-cycle. To facilitate its more comprehensive and more effective
usage, a checklist for a guided process is described. The chapter uses a
running example Asymmetric Release Planning to illustrate the whole process. A
Return-on-Investment analysis is proposed as part of the problem scoping. This
helps to decide on the depth and breadth of analysis in relation to the effort
needed to run the analysis and the projected value of the solution.
