IJARCCE - Computer and Communication Engineering

Abstract

1,2,3,4 Abstract: The project consists of an efficient Currently Many researchers have addressed this important problem with varying end goals and have proposed valuation techniques to compute the total number of faults. A group of researchers focuses on finding error-prone modules base on the size of the module. Briand et al. Predict the number of remaining faults during inspection using actual inspection data, where as Strand et al predict which files will contain the most faults in the next release. Zhang and Mockus use data collected from previous projects to estimate the number of faults in a new project. However, these data sets are not always available or, even if they are, may lead to inaccurate estimates. For example, Zhang and Mockus use a naive method base only on the size of the product to select similar projects while ignoring many other critical factors such as project type, complexity, etc. Another alternative that appears to produce very accurate estimates is base on the use of Bayesian Belief Networks (BBNs) .However, these techniques require the use of additional information, such as expert knowledge and empirical data, that are not necessarily collected by most software development companies. Software reliability growth models (SRGMs) are also used to estimate the total number of faults to measure software reliability. Although they can be used to indicate the status of the testing process, some have slow convergence while others have limited application as they may require more input data or initial values that are selected by experts.