Scalable Quality Assurance for Startups: Design and Case Study

Startups engaged in digital transformation face distinctive Quality Assurance (QA) challenges due to rapid development, evolving requirements, and limited resources. Traditional QA approaches, designed for stable and well-resourced environments, often introduce overheads that are impractical for early-stage companies. This thesis proposes a scalable and modular QA framework tailored to startup conditions, aiming to embed structured yet adaptable quality practices without compromising agility. The framework was designed by combining insights from a five-month QA internship at a startup with a review of the academic literature. Eight foundational pillars emerged: lightweight process, hybrid testing strategies, modular and risk-based testing, continuous testing via CI/CD, artificial intelligence and cloud-based support, documentation, regulatory compliance (GDPR and accessibility), and KPI-driven monitoring. These pillars shaped the literature review and validated practical relevance. To enhance applicability, the pillars were organized into seven actionable QA domains: QA Process, QA Strategies, QA Techniques, Tools and Enablers, Metrics and Feedback, Compliance and Ethics, and Organizational Enablement—forming the scalable Digital Transformation Startup Quality Assurance Framework (DTS-QAF). This domain structure offers startups a flexible reference model for phased adoption based on their needs. Applied in a real-world startup, the framework improved test coverage, documentation clarity, and feedback time while supporting risk prioritization. It also surfaced ongoing challenges around automation strategy and metrics tracking. This thesis contributes a context-aware QA framework, offering startups a practical path to maturing their QA practices. It also sets a foundation for future research into lightweight methodologies, incremental automation, and the integration of compliance quality practices within startup teams.