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.

The paper studies practical reliability measurement and modeling for large commercial software systems based on test execution data collected during system testing. The application environment and the goals of reliability assessment were analyzed to identify appropriate measurement data. Various reliability growth models were used on failure data normalized by test case executions to track testing progress and provide reliability assessment. Practical problems in data collection, reliability measurement and modeling, and modeling result analysis were also examined. The results demonstrated the feasibility of reliability measurement in a large commercial software development environment and provided a practical comparison of various reliability measurements and models under such an environment.>