Compara mètodes

	Model de Predicció de Defectes	Seguiment de la velocitat àgil
Camp	Enginyeria de programari	Enginyeria de programari
Família	Process / pipeline	Process / pipeline
Any d'origen	2005	2002
Autor original	Thomas Ostrand, Elaine Weyuker, Robert Bell	Ken Schwaber and Mike Cohn
Tipus	machine learning model	measurement metric
Font seminal	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.
Àlies	fault prediction, bug prediction, defect classification	sprint velocity, team capacity planning, burndown analysis
Relacionats	4	4
Resum	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.