विधियों की तुलना करें

	आर्किटेक्चर स्मेल डिटेक्शन	दोष पूर्वानुमान मॉडल
क्षेत्र	सॉफ्टवेयर इंजीनियरिंग	सॉफ्टवेयर इंजीनियरिंग
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	2009	2005
प्रवर्तक	Martin Fowler and García et al.	Thomas Ostrand, Elaine Weyuker, Robert Bell
प्रकार	pattern-based analysis	machine learning model
मौलिक स्रोत	Fowler, M. (2018). Code smell. Martin Fowler's Website.	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.
उपनाम	design smell detection, architectural debt analysis, system quality assessment	fault prediction, bug prediction, defect classification
संबंधित	4	4
सारांश	Architecture smells are recurring patterns in system structure that indicate potential design problems. Introduced by García et al. (2009), these patterns signal violations of architectural principles (modularity, independence, abstraction) at system scale. Detection combines code metrics, dependency analysis, and pattern recognition to identify smells early, guiding refactoring and architectural improvements.	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.