পদ্ধতির তুলনা করুন

	ত্রুটি পূর্বাভাস মডেল	সফ্টওয়্যার জটিলতা মেট্রিক্স
ক্ষেত্র	সফটওয়্যার প্রকৌশল	সফটওয়্যার প্রকৌশল
পরিবার	Process / pipeline	Process / pipeline
উদ্ভবের বছর	2005	1976
প্রবর্তক	Thomas Ostrand, Elaine Weyuker, Robert Bell	Thomas J. McCabe
ধরন	machine learning model	quantitative measurement
মৌলিক উৎস	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.
অপর নাম	fault prediction, bug prediction, defect classification	code complexity analysis, complexity measurement
সম্পর্কিত	4	4
সারসংক্ষেপ	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.