방법 비교

	결함 예측 모델	소프트웨어 복잡도 측정 지표
분야	소프트웨어공학	소프트웨어공학
계열	Process / pipeline	Process / pipeline
기원 연도	2005	1976
창시자	Thomas Ostrand, Elaine Weyuker, Robert Bell	Thomas J. McCabe
유형	machine learning model	quantitative measurement
원전	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.
별칭	fault prediction, bug prediction, defect classification	code complexity analysis, complexity measurement
관련	4	4
요약	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.