Improving Effort Estimation Accuracy by Weighted Grey Relational Analysis During Software Development

Abstract

Grey relational analysis (GRA), a similarity-based method, presents acceptable prediction performance in software effort estimation. However, we found that conventional GRA methods only consider non-weighted conditions while predicting effort. Essentially, each feature of a project may have a different degree of relevance in the process of comparing similarity. In this paper, we propose six weighted methods, namely, non-weight, distance-based weight, correlative weight, linear weight, nonlinear weight, and maximal weight, to be integrated into GRA. Three public datasets are used to evaluate the accuracy of the weighted GRA methods. Experimental results show that the weighted GRA performs better precision than the non-weighted GRA. Specifically, the linearly weighted GRA greatly improves accuracy compared with the other weighted methods. To sum up, the weighted GRA not only can improve the accuracy of prediction but is an alternative method to be applied to software development life cycle.