Bandingkan metode

	Pelacakan Kecepatan Agile	Model Prediksi Cacat
Bidang	Rekayasa Perangkat Lunak	Rekayasa Perangkat Lunak
Keluarga	Process / pipeline	Process / pipeline
Tahun asal	2002	2005
Pencetus	Ken Schwaber and Mike Cohn	Thomas Ostrand, Elaine Weyuker, Robert Bell
Tipe	measurement metric	machine learning model
Sumber perintis	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.
Alias	sprint velocity, team capacity planning, burndown analysis	fault prediction, bug prediction, defect classification
Terkait	4	4
Ringkasan	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.