Control software is increasingly essential for the functionality of automated production systems (aPS), necessitating the reuse of high-quality software to maintain competitiveness and ensure systems are evolvable, maintainable, and reconfigurable. Despite the benefits of software quality assessment methods, such as static code analysis and software metrics, their adoption in the aPS domain remains limited, and current support for applying these methods to control software is inadequate. This thesis addresses this gap by proposing a comprehensive quality assessment procedure tailored for control software. It includes interview guiding questions, an analysis checklist, and visualization tools for documenting analysis results. This procedure enables developers to incorporate static analysis techniques from computer science into their workflows, facilitating software quality assessment and identifying improvement opportunities. It allows for customization based on specific company conditions, taking into account the rationale behind the code, intentional design choices, and domain expertise. The proposed procedure was validated through a prototypical implementation, three industrial case studies, and feedback from two expert groups, confirming its effectiveness in real-world industrial projects and its utility in identifying areas for improvement and actionable recommendations.
