Порівняння методів

	Відстеження швидкості команди	Модель прогнозування дефектів
Галузь	Програмна інженерія	Програмна інженерія
Родина	Process / pipeline	Process / pipeline
Рік появи	2002	2005
Автор методу	Ken Schwaber and Mike Cohn	Thomas Ostrand, Elaine Weyuker, Robert Bell
Тип	measurement metric	machine learning model
Основоположне джерело	Schwaber, K., & Beedle, M. (2002). Agile Software Development with Scrum. Prentice Hall.	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.
Інші назви	sprint velocity, team capacity planning, burndown analysis	fault prediction, bug prediction, defect classification
Пов'язані	4	4
Підсумок	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.	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.