Comparer des méthodes

	Modèle de prédiction de défauts	Suivi Agile de la vélocité
Domaine	Génie logiciel	Génie logiciel
Famille	Process / pipeline	Process / pipeline
Année d'origine	2005	2002
Auteur d'origine	Thomas Ostrand, Elaine Weyuker, Robert Bell	Ken Schwaber and Mike Cohn
Type	machine learning model	measurement metric
Source fondatrice	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.
Alias	fault prediction, bug prediction, defect classification	sprint velocity, team capacity planning, burndown analysis
Apparentées	4	4
Résumé	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.