Software reliability: measurement, prediction, application
Software reliability: measurement, prediction, application
Software Reliability Model with Optimal Selection of Failure Data
IEEE Transactions on Software Engineering - Special issue on software reliability
Handbook of software reliability engineering
Test workload measurement and reliability analysis for large commercial software systems
Annals of Software Engineering
Test-Execution-Based Reliability Measurement and Modeling for Large Commercial Software
IEEE Transactions on Software Engineering
Integrating Time Domain and Input Domain Analyses of Software Reliability Using Tree-Based Models
IEEE Transactions on Software Engineering
A logarithmic poisson execution time model for software reliability measurement
ICSE '84 Proceedings of the 7th international conference on Software engineering
Analysis of error processes in computer software
Proceedings of the international conference on Reliable software
Testing for software reliability
Proceedings of the international conference on Reliable software
Software Reliability Modeling by Concatenating Failure Rates
ISSRE '98 Proceedings of the The Ninth International Symposium on Software Reliability Engineering
Evaluating Web Software Reliability Based on Workload and Failure Data Extracted from Server Logs
IEEE Transactions on Software Engineering
Modified adaptive resonance theory network for mixed data based on distance hierarchy
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
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This paper presents a new approach to software reliability modeling by grouping data into clusters of homogeneous failure intensities. This series of data clusters associated with different time segments can be directly used as a piecewise linear model for reliability assessment and problem identification, which can produce meaningful results early in the testing process. The dual model fits traditional software reliability growth models (SRGMs) to these grouped data to provide long-term reliability assessments and predictions. These models were evaluated in the testing of two large software systems from IBM. Compared with existing SRGMs fitted to raw data, our models are generally more stable over time and produce more consistent and accurate reliability assessments and predictions.