Abstract: This thesis addresses the critical conflict between data-driven software engineering analytics and individual privacy rights under the General Data Protection Regulation (GDPR). The MECOIS project transforms vast development meta- data into actionable productivity insights, yet the granular nature of this information poses significant privacy risks. To resolve this, this work designs and implements a prototype data anonymization service as a decoupled, containerized system within the MECOIS architecture. The service utilizes a proxy-based approach for identity management, pseudonymizing direct identifiers into deterministic Universally Unique Identifiers (UUIDs) and achieving k-anonymity by grouping contributors into organizational equivalence classes. Evaluation of the prototype indicates that it successfully maintains data integrity for high-dimensional metadata while effectively mitigating the risk of ”singling out” individuals. Ultimately, this study provides a scalable and legally grounded foundation for software analytics that respects developer privacy.
Keywords: anonymization, identities
PDF: Master Thesis
Reference: Tobias Kaiser. Enhancing UX and Developer Experience in MECOIS through a Scalable AI-Augmented Design System for Engineering Analytics Platforms. Master Thesis. Friedrich-Alexander-Universität Erlangen-Nürnberg: 2026.
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