>> New series 2025: Problems with quality assurance
A lack of standards, undetected defects, and delayed quality assurance jeopardize many projects. Quality is often only checked at the end, when changes are expensive or no longer possible. However, quality can be systematically ensured right from the start.
If quality is not planned and checked, there is a risk of errors being discovered late, resulting in a high amount of rework, differing quality standards within the team, a lack of reproducibility, and a lack of standards.
Introduce checklists and the dual control principle
Simple but effective: checklists ensure standards are met, for example during reviews, releases, or approvals. The dual control principle reduces sources of error and reveals blind spots.
Establish a quality management plan
A documented plan defines binding quality targets, roles, processes, and checkpoints in the project. It ensures clarity and traceability, especially in interdisciplinary teams.
Use CIP cycles and root cause analyses
Quality is not a one-time goal, but an ongoing process. CIP cycles (continuous improvement process) help to learn systematically from mistakes. Root cause analyses identify causes, not just symptoms.
AI-supported tools can automate and objectify quality assurance, particularly in IT, software, and data-driven projects. Possible areas of application include:
Analysis of source code for errors, complexity, or security vulnerabilities
Automated testing and regression testing
Early detection of patterns that typically lead to quality problems
Examples of AI-supported testing tools:
Examine existing code for potential security vulnerabilities and technical debt. Which parts of the code contain anti-patterns or complex, difficult-to-maintain structures? Use static code analysis and semantic evaluation with AI.
Analyze automated tests from the last four sprints. Where were the most frequent sources of error? Which modules or components are particularly vulnerable? Which tests need to be supplemented or adapted?
Put yourself in the shoes of a QA team working on a complex project. Use NLP to analyze error reports and user feedback for recurring problems. What causes can be deduced from this? Where does root cause analysis make sense?
Use AI to evaluate whether the current review and approval processes (dual control principle) are sufficiently effective. Where do corrections accumulate despite review? Which steps in the review process should be supplemented or automated?
Conclusion: Quality comes from structure, not luck
Effective quality assurance begins with clear standards and responsibilities. Tools such as checklists and a dual control principle have a rapid effect. In the long term, CIP cycles and a structured quality management plan unfold their full effect.
Additionally, utilize AI to make quality measurable, scalable, and future-proof, from the first line of code to the final review.
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