পদ্ধতির তুলনা করুন

	সফ্টওয়্যার জটিলতা মেট্রিক্স	ত্রুটি পূর্বাভাস মডেল
ক্ষেত্র	সফটওয়্যার প্রকৌশল	সফটওয়্যার প্রকৌশল
পরিবার	Process / pipeline	Process / pipeline
উদ্ভবের বছর	1976	2005
প্রবর্তক	Thomas J. McCabe	Thomas Ostrand, Elaine Weyuker, Robert Bell
ধরন	quantitative measurement	machine learning model
মৌলিক উৎস	McCabe, T. J. (1976). A complexity measure. IEEE Transactions on Software Engineering, 2(4), 308–320.	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.
অপর নাম	code complexity analysis, complexity measurement	fault prediction, bug prediction, defect classification
সম্পর্কিত	4	4
সারসংক্ষেপ	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.	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.