Porovnať metódy

	Model predikcie chýb	Metriky zložitosti softvéru
Odbor	Softvérové inžinierstvo	Softvérové inžinierstvo
Rodina	Process / pipeline	Process / pipeline
Rok vzniku	2005	1976
Tvorca	Thomas Ostrand, Elaine Weyuker, Robert Bell	Thomas J. McCabe
Typ	machine learning model	quantitative measurement
Pôvodný zdroj	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.
Ďalšie názvy	fault prediction, bug prediction, defect classification	code complexity analysis, complexity measurement
Príbuzné	4	4
Zhrnutie	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.