Software effort estimation based on weighted fuzzy grey relational analysis
PROMISE '09 Proceedings of the 5th International Conference on Predictor Models in Software Engineering
Fuzzy grey relational analysis for software effort estimation
Empirical Software Engineering
Comparison of weighted grey relational analysis for software effort estimation
Software Quality Control
Predicting software project effort: A grey relational analysis based method
Expert Systems with Applications: An International Journal
Recent methods for software effort estimation by analogy
ACM SIGSOFT Software Engineering Notes
Automated trendline generation for accurate software effort estimation
Proceedings of the 3rd annual conference on Systems, programming, and applications: software for humanity
Grey relational effort analysis technique using robust regression methods for individual projects
International Journal of Computational Intelligence Studies
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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.