The machine and plant manufacturing sector is considered one of the most important industries worldwide. However, ever shorter innovation cycles coupled with intensified international competition lead to high pressure on companies to remain globally successful. In the course of the digital transformation in machine and plant manufacturing, automation software is gaining increasing importance as a rising proportion of system functionality is implemented via software. Thus, high-quality, mature automation software architectures are one of the key prerequisites for companies to stay competitive in the long term. An essential lever for achieving this goal is to capture and quantify the impact of architectural design decisions on automation software quality by considering the company-specific boundary conditions. So far, automation software developers mostly make design decisions based on their experience. However, quantitative measurement methods for software quality from static code analysis have proven beneficial in supporting subjective, experience-based decisions with numerical indicators such as software quality metrics. Thus, an analysis concept for automation software architecture is provided that combines a qualitative approach to identify design decisions and their intentions in real-world industrial PLC software with a quantitative, metric-based approach to measure the degree of selected quality attributes of automation software. The applicability and benefits in industrial software development workflows are confirmed in machine and plant manufacturing companies as well as platform providers.weiterlesen