Effect of data preprocessing on software effort estimation
This work addresses accuracy challenges in software project planning for organizations, but it is incremental as it compares simple existing preprocessing techniques.
The paper tackled the problem of improving software effort estimation accuracy by comparing three data preprocessing methods (none, norm, log) using the COCOMO model, finding that the norm preprocessor was more accurate than the others.
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 study, 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.