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In the software reliability growth phase, the nature of the failure data is, in a sense, determined by the software testing process. A hybrid model is proposed for medium and long-term software failure time forecasting in this paper. The hybrid model consists of two methods, Singular Spectrum Analysis (SSA) and Auto Regressive Integrated Moving Average(ARIMA). In this model, the time series of software...
In recent years, with its sustained and rapid economic development, the contradiction between supply and demand on China's petroleum is daily outstanding, and it makes the dependence rate of foreign oil resources going higher and higher. With the method of time series analysis and based on the data of China's oil self-sufficiency and proven reserves from 1980 to 2009, ARIMA models which is used for...
To observe and study the performance degradation of the application server, client-request programs and server monitoring programs are designed for different scenarios to record different parameters - five categories and 36 parameters altogether. In this paper, primary component analysis method is adopted to reduce dimension, and then multi-dimensional time series analysis method used to set up a...
The usage of the Internet has become ubiquitous, even for desktop applications to assume that the computer system it is running on is connected to the Internet. Desktop applications rely on the Internet connectivity for software license authentication and also for maintenance through downloading of software patches. However, the latter can pose an annoyance to the user when he or she is relying on...
An evolutionary regression modeling approach for software cumulative failure prediction based on auto-regression order 4, 7 and 10 models are proposed. A real coded genetic algorithm is used to optimize the mean square of the error produced by training the auto-regression model. In this paper, we present a real coded genetic algorithm that uses the appropriate operators for this encoding type to train...
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