So sánh phương pháp

	Mô hình dự đoán lỗi	Theo dõi Vận tốc Nhanh nhẹn
Lĩnh vực	Kỹ thuật phần mềm	Kỹ thuật phần mềm
Họ	Process / pipeline	Process / pipeline
Năm ra đời	2005	2002
Người khởi xướng	Thomas Ostrand, Elaine Weyuker, Robert Bell	Ken Schwaber and Mike Cohn
Loại	machine learning model	measurement metric
Công trình gốc	Ostrand, T. J., Weyuker, E. J., & Bell, R. M. (2005). Predicting the location and number of faults in large software systems. IEEE Transactions on Software Engineering, 31(4), 340–355.	Schwaber, K., & Beedle, M. (2002). Agile Software Development with Scrum. Prentice Hall.
Tên gọi khác	fault prediction, bug prediction, defect classification	sprint velocity, team capacity planning, burndown analysis
Liên quan	4	4
Tóm tắt	Defect prediction models forecast the likelihood of software faults in code modules using statistical or machine learning approaches. Pioneered by Ostrand, Weyuker, and Bell (2005), these models correlate code metrics (complexity, churn, coupling) with historical defect data to identify high-risk components. Organizations use predictions to allocate testing resources, guide code review, and prioritize refactoring.	Velocity tracking measures the amount of work (typically story points or tasks) a team completes in a sprint, enabling capacity planning, release forecasting, and identification of process improvements. Introduced in Scrum methodology by Schwaber (2002), velocity provides empirical data for realistic sprint planning and project timeline prediction. Teams use velocity trends to identify bottlenecks and validate process improvements.