Linganisha mbinu

	Modeli ya Ut napaji wa Kasoro	Vipimo vya Utata wa Programu
Nyanja	Uhandisi wa Programu	Uhandisi wa Programu
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	2005	1976
Mwanzilishi	Thomas Ostrand, Elaine Weyuker, Robert Bell	Thomas J. McCabe
Aina	machine learning model	quantitative measurement
Chanzo asilia	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.	McCabe, T. J. (1976). A complexity measure. IEEE Transactions on Software Engineering, 2(4), 308–320.
Majina mbadala	fault prediction, bug prediction, defect classification	code complexity analysis, complexity measurement
Zinazohusiana	4	4
Muhtasari	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.	Software complexity metrics quantify the structural and operational difficulty of code through numerical measurements. Introduced by Thomas McCabe in 1976, cyclomatic complexity became the foundational approach. These metrics assess maintainability, testability, and defect risk, enabling teams to identify problematic code regions and guide refactoring efforts.