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| Modello di affidabilità del software× | Modelli di Previsione dei Difetti× | |
|---|---|---|
| Campo | Ingegneria del software | Ingegneria del software |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 1979 | 2005 |
| Ideatore≠ | Alok Goel and Kazuhira Okumoto | Thomas Ostrand, Elaine Weyuker, Robert Bell |
| Tipo≠ | stochastic model | machine learning model |
| Fonte seminale≠ | Goel, A. L., & Okumoto, K. (1979). Time-dependent error-detection rate model for software reliability and other performance measures. IEEE Transactions on Reliability, 28(3), 206–211. DOI ↗ | 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. DOI ↗ |
| Alias | reliability growth model, failure rate prediction, SRGM | fault prediction, bug prediction, defect classification |
| Correlati | 4 | 4 |
| Sintesi≠ | Software reliability models predict the behavior of failure rates during testing and operation, estimating when software achieves required reliability targets. Introduced by Goel and Okumoto (1979), these stochastic models capture how defect discovery declines as testing progresses. Organizations use reliability models to forecast release readiness, estimate testing duration, and validate quality achievement. | 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. |
| ScholarGateInsieme di dati ↗ |
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