Comparison for the accuracy of defect fix effort estimation

Software defects have become the dominant cause of customer outage and the study of software errors has been necessitated by the emphasis on software reliability. Yet, software defects are not well enough understood to provide a clear methodology for avoiding or recovering from them. So software defect fix efforts play the critical role in software quality assurance. In this paper, the comparison of the defect fix effort estimation by using Self-Organizing Map in neural network is presented. To estimate the defect fix effort, KC3 static defect attributes that is one of the publicly available data from NASA products is used in this paper. To implement the SOM, finding different clusters from the SOM and for the calculation of fix effort time of defect SOM Toolbox in MATLAB 7.0.1 is used.