Name
Abstract | ||
|---|---|---|
Most software reliability growth models work under the assumption that reliability of software grows due to the removal of bugs that cause failures. However, another phenomenon has often been observed—the failure rate of a software product following its release decreases with time even if no bugs are corrected. In this article we present a simple model to represent this phenomenon. We introduce the concept of initial transient failure rate of the product and assume that it decays with a factor α per unit time thereby increasing the product reliability with time. When the transient failure rate decays away, the product displays a steady state failure rate. We discuss how the parameters in this model—initial transient failure rate, decay factor, and steady state failure rate—can be determined from the failure and sales data of a product. We also describe how, using the model, we can determine the product stabilization time—a product quality metric that describes how long it takes a product to reach close to its stable failure rate. We provide many examples where this model has been applied to data from released products. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1145/13487689.13487690 | ACM Transactions on Software Engineering and Methodology (TOSEM) |
Keywords | Field | DocType |
stable failure rate,post-release reliability growth,software product,product quality metric,product reliability,cause failure,steady state failure rate,product stabilization time,initial transient failure rate,failure rate,transient failure rate,steady state | Computer science,Failure rate,Software,Steady state,Software reliability growth,Reliability engineering | Journal |
Volume | Issue | ISSN |
17 | 4 | 1049-331X |
Citations | PageRank | References |
4 | 0.46 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pankaj Jalote | 1 | 791 | 182.69 |
| Brendan Murphy | 2 | 1193 | 45.91 |
| Vibhu Saujanya Sharma | 3 | 174 | 21.65 |