Ayalew BelayNigusu Daniel2026-09-022026-09-022025https://etd.aau.edu.et/handle/123456789/8946This thesis investigates the enhancement of quality assurance (QA) in agile software development, with a specific focus on the Scrum framework. Agile methodologies, while effective in promotingflexibility and fast delivery, often face challenges in maintaining consistent software quality due to limited time for testing and the absence of structured QA practices. Traditional quality modelsare often incompatible with the iterative and dynamic nature of agile processes.To address this issue, the study proposes a data-driven conceptual framework that integrates quality assurance checkpoints into Scrum-based agile workflows. The framework is built upon twoestablished quality models: the Plan–Do–Check–Act (PDCA) cycle and the Cross-IndustryStandard Process for Data Mining (CRISP-DM). These models guide the systematic placementand evaluation of QA checkpoints across the Software Development Life Cycle and the ProjectLife Cycle.The research followed a mixed-methods approach. The framework was initially developed throughan in-depth literature review and expert consultations (qualitative phase), and then empiricallyvalidated using a quantitative survey of 51 agile professionals in Addis Ababa. The structuredquestionnaire captured data on QA practices across Scrum phases, and statistical analysis usingSPSS including correlation analysis was applied to identify critical checkpoints and evaluate theframework’s relevance.Results show that integrating structured, data-driven QA checkpoints into Scrum significantlyenhances defect tracking, test planning, and product quality. The proposed framework provides ascalable and practical solution for organizations seeking to align agile delivery with rigorousquality standards.enAgile Software DevelopmentScrumQuality AssuranceData-Driven FrameworkSoftware QualityPDCACRISP-DMThesis