Fuzzy grey relational analysis for software effort estimation

  • Authors:

  • Mohammad Azzeh;Daniel Neagu;Peter I. Cowling

  • Affiliations:

  • AI Research Centre, Department of Computing, University of Bradford, Bradford, UK BD7 1DP;AI Research Centre, Department of Computing, University of Bradford, Bradford, UK BD7 1DP;AI Research Centre, Department of Computing, University of Bradford, Bradford, UK BD7 1DP

  • Venue:
  • Empirical Software Engineering
  • Year:
  • 2010

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Abstract

Accurate and credible software effort estimation is a challenge for academic research and software industry. From many software effort estimation models in existence, Estimation by Analogy (EA) is still one of the preferred techniques by software engineering practitioners because it mimics the human problem solving approach. Accuracy of such a model depends on the characteristics of the dataset, which is subject to considerable uncertainty. The inherent uncertainty in software attribute measurement has significant impact on estimation accuracy because these attributes are measured based on human judgment and are often vague and imprecise. To overcome this challenge we propose a new formal EA model based on the integration of Fuzzy set theory with Grey Relational Analysis (GRA). Fuzzy set theory is employed to reduce uncertainty in distance measure between two tuples at the k th continuous feature $$ \left( {\left| {\left( {{x_o}(k) - {x_i}(k)} \right.} \right|} \right) $$ .GRA is a problem solving method that is used to assess the similarity between two tuples with M features. Since some of these features are not necessary to be continuous and may have nominal and ordinal scale type, aggregating different forms of similarity measures will increase uncertainty in the similarity degree. Thus the GRA is mainly used to reduce uncertainty in the distance measure between two software projects for both continuous and categorical features. Both techniques are suitable when relationship between effort and other effort drivers is complex. Experimental results showed that using integration of GRA with FL produced credible estimates when compared with the results obtained using Case-Based Reasoning, Multiple Linear Regression and Artificial Neural Networks methods.