Data partition based reliability modeling

Abstract

The paper presents an approach to software reliability modeling using data partitions derived from tree based models. We use these data sensitive partitions to group data into clusters with similar failure intensities. The series of data clusters associated with different time segments forms a piecewise linear model for the assessment and short term prediction of reliability. Long term prediction can be provided by the dual model that uses these grouped data as input fitted to some failure count variations of the traditional software reliability growth models. These partition based reliability models can be used effectively to measure and predict the reliability of software systems and can be readily integrated into our strategy of reliability assessment and improvement using tree based modeling.