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 in real life – the failure rate of a software product following its release decreases with time irrespective of whether bugs are corrected or not. In this work we discuss reasons for this phenomenon and present a simple model to represent it. 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. Applying this failure rate decay model to the failure and sales data of a product, it is possible to determine its initial transient failure rate, decay factor, and steady state failure rate. We can also 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 also provide examples where this model has been applied to failure data captured from released products.

Joint work with: B. Murphy, Microsoft Research, Cambridge; Vibhu Sharma, PhD Scholar CSE, IIT Kanpur

Bio:
Director, IIIT Delhi.