Adequacy, Accuracy, Scalability, and Uncertainty of Architecture-based Software Reliability: Lessons Learned from Large Empirical Case Studies
Katerina Goseva-Popstojanova, Margaret Hamill and Xuan Wang
17th International Symposium on Software Reliability Engineering (ISSRE 06)
Raleigh, North Carolina, USA, November 6-11, 2006
Abstract
Our earlier research work on applying architecture-based software reliability models on a large scale case study allowed us to test how and when they work, to understand their limitations, and to outline the issues that need attention in future research studies. In this paper we first present an additional case study which confirms our earlier findings. Then, we present uncertainty analysis of architecture-based software reliability using the empirical results from both case studies. The results show that Monte Carlo method scales better than the method of moments. The sensitivity analysis based on Monte Carlo method shows that a small number of parameters contribute to the most of the variation in system reliability. Even more, components' reliabilities have more significant impact on system reliability than transition probabilities.