Software reliability: measurement, prediction, application
Software reliability: measurement, prediction, application
Reliability engineering handbook (vol. 1)
Reliability engineering handbook (vol. 1)
Handbook of software reliability engineering
Handbook of software reliability engineering
Measuring and Modeling Usage and Reliability for Statistical Web Testing
IEEE Transactions on Software Engineering - Special section on the seventh international software metrics symposium
An Empirical Method for Selecting Software Reliability Growth Models
Empirical Software Engineering
Summary of WWW characterizations
World Wide Web
Software reliability growth analysis-application of NHPP models and its evaluation
HASE '96 Proceedings of the 1996 High-Assurance Systems Engineering Workshop
A Comparison and Integration of Capture-Recapture Models and the Detection Profile Method
ISSRE '98 Proceedings of the The Ninth International Symposium on Software Reliability Engineering
Exploring Defect Data from Development and Customer Usage on Software Modules over Multiple Releases
ISSRE '98 Proceedings of the The Ninth International Symposium on Software Reliability Engineering
Software Reliability Growth Models Incorporating Fault Dependency with Various Debugging Time Lags
COMPSAC '04 Proceedings of the 28th Annual International Computer Software and Applications Conference - Volume 01
Evaluating Web Software Reliability Based on Workload and Failure Data Extracted from Server Logs
IEEE Transactions on Software Engineering
Empirical Characterization of Session---Based Workload and Reliability for Web Servers
Empirical Software Engineering
Prediction of atomic web services reliability based on k-means clustering
Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering
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In this paper, we analyze web traffic characters and the relationship with software failures. Results indicate hourly web access traffic is the lowest from 3:00 to 4:00 am, while the traffic load gradually reaches peak between 14:00 and 16:00, before declining. In daily base, web traffic fluctuates in the 25 observed days. The hourly access hits appear in similar patterns to the software failures. The web site reliability is 0.9878. The mean time between failures is 82.03 hits. Five popular software reliability models are calibrated with real data. The validations show that Goel-Okumoto and Gompertz models accurately describe web software failures. Further investigations indicate that both models have some deviations in prediction accuracy starting from the 20th day. Using similar approach to change-point solutions, we recalibrate the models with different parameter values after 20th day. The results appear that two sets of parameter values greatly improve model prediction accuracy.