For the successful development of software and thus the success of a pro-ject, a software engineering methodod (SEM) is first tailored to the project situation. Despite that up to 75% of projects in the IT enviroment fail during their enactment. Deadlines are exceeded as well as the budget of the project. The implementation of the software engineering method, changes to the project situation or lack of quality endanger the success of the project. Espe-cially time is a critical factor to detect problems and adapt a SEM in time. A continous monitoring of the software engineering method is needed as well as a dynamic and autonomous adaptation. Approaches as Situational Method Engineering, Six Sigma or the PDCA-Cycle, also known as Deming Cycle, are well-known for the improvement and adaptation of software engineering methods. However, due to their long implementation duration they are hardly suitable for such an adjustment. Furthermore, these procedures are typically performed before or after problems are detected and the project has failed. Though agile methods like Scrum or project controlling use first approaches for inspection and adaptation of a running project, they are not usable for an autonomous adaptation due to lack of automating possibilities. Unlike Six Sigma, the Deming Cycle or the Agile Methods, approaches from the self-adaptive systems domain observe systems automatically at runtime using feedback loops and adapt the system autonomously if necessary. The best-known model is called the MAPE-K loop. This PhD thesis presents with MAPE-K4SEM an approach that uses the MAPE-K loop for a continous monitoring and automatic adaption of software engineering me-thod and thus makes a self-adaptive software engineering method possible.