Bandingkan metode

	Deteksi Bau Arsitektur	Model Prediksi Cacat
Bidang	Rekayasa Perangkat Lunak	Rekayasa Perangkat Lunak
Keluarga	Process / pipeline	Process / pipeline
Tahun asal	2009	2005
Pencetus	Martin Fowler and García et al.	Thomas Ostrand, Elaine Weyuker, Robert Bell
Tipe	pattern-based analysis	machine learning model
Sumber perintis	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.
Alias	design smell detection, architectural debt analysis, system quality assessment	fault prediction, bug prediction, defect classification
Terkait	4	4
Ringkasan	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.