Final Thesis: Building a Quality Assurance Dashboard for Data Pipelines

Abstract: This thesis presents a lightweight, GitHub-native quality assurance (QA) system for data pipelines. The
solution consists of two artifacts: a QA dashboard that summarizes the current quality state, and a QA pull request comment bot that shows change-focused metrics. Metrics focus on four quality characteristics: maintainability, functional suitability, performance efficiency, and security. A configuration-driven pipeline turns raw metrics into stable JSON artifacts (baseline and deltas) and renders them as Markdown, keeping results reproducible and easy to review. The approach remains extensible. Promising next steps include performance testing, more metrics, and refined visualization.

Keywords: quality-assurance, ci-cd, engineering-analytics, testing

PDF: Master Thesis

Reference: Celine Pöhl. Building a Quality Assurance Dashboard for Data Pipelines. Master Thesis. Friedrich-Alexander-Universität Erlangen-Nürnberg: 2025.