Provided by: Cornell University
Topic: Data Management
Date Added: May 2013
Software effort estimation requires high accuracy, but accurate estimations are difficult to achieve. Increasingly, data mining is used to improve an organization's software process quality, e.g. the accuracy of effort estimations .There are a large number of different method combination exists for software effort estimation, selecting the most suitable combination becomes the subject of research in this paper. In this paper, three simple preprocessors are taken (none, norm, log) and effort is measured using COCOMO model. Then results obtained from different preprocessors are compared and norm preprocessor proves to be more accurate as compared to other preprocessors.