The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In order to assure software quality and assess software reliability, many software reliability growth models (SRGMs) have been proposed for estimation of reliability growth of products in the past three decades. In principle, two widely used methods for the parameter estimation of SRGMs are the maximum likelihood estimation (MLE) and the least squares estimation (LSE). However, the approach of these...
In this paper, we propose a modified genetic algorithm (MGA) with calibrating fitness functions, weighted bit mutation, and rebuilding mechanism for the parameter estimation of software reliability growth models (SRGMs). An example using a real failure data is given to demonstrate the performance of proposed method. Experimental result shows that MGA is effective for estimating the parameters of SRGM.
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.