SCIP is a solver for mathematical optimization problems developed at the Zuse Institute Berlin (scip.zib.de). It is openly available for academic use and widely employed in research and education at many institutes and universities around the world. For commercial use, a license fee is charged. The source code is version controlled with git, while a continuous integration and regular tests ensure correctness. Theoretical ideas developed in theses and research articles are implemented in SCIP to analyse their impact on functionality and performance. Two Python tools were developed for this analysis. In this talk we want to introduce both SCIP and these tools with a focus on their interaction and the development process.