On the relative value of cross-company and within-company data for defect prediction
Empirical Software Engineering
On the value of learning from defect dense components for software defect prediction
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
Architecture-based reliability evaluation under uncertainty
Proceedings of the joint ACM SIGSOFT conference -- QoSA and ACM SIGSOFT symposium -- ISARCS on Quality of software architectures -- QoSA and architecting critical systems -- ISARCS
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Uncertainty analysis through sensitivity studies and quantification of the variance of the reliability estimate has become more common in architecture-based software reliability studies. However, up to this point no attempts have been made to explicate the results of such analysis. Our earlier work based on several medium to large scale empirical studies showed that a very few parameters have a significant impact on the variability of system reliability. This paper explains the reasons behind this phenomenon. Unlike related work that considered the impact of the parameters on software reliability either through their model sensitivity or through uncertainty of their estimates, we consider both. Furthermore, we look at all parameters, i.e., components reliabilities and probabilities of transfer of control between components. Based on theoretical and empirical arguments, we justify why a few parameters contribute most of the variance of the reliability estimate. Comparing our results with those obtained through simple model sensitivity studies shows that such studies are not always sufficient to accurately quantify the impact of critical components on variability of system reliability.