Salīdzināt metodes

	Arhitektūras smaku noteikšana	Defektu prognozēšanas modelis
Nozare	Programmatūras inženierija	Programmatūras inženierija
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	2009	2005
Autors	Martin Fowler and García et al.	Thomas Ostrand, Elaine Weyuker, Robert Bell
Tips	pattern-based analysis	machine learning model
Pirmavots	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.
Citi nosaukumi	design smell detection, architectural debt analysis, system quality assessment	fault prediction, bug prediction, defect classification
Saistītās	4	4
Kopsavilkums	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.