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Analysis of cluster center initialization of 2FA-kprototypes analogy-based software effort estimation

Abstract:

Analogy-based estimation is one of the most widely used techniques for effort pbkp_rediction in software engineering. However, existing analogy-based techniques suffer from an inability to correctly handle nonquantitative data. To deal with this limitation, a new technique called 2FA-kprototypes was proposed and evaluated. 2FA-kprototypes is based on the use of the fuzzy k-prototypes clustering technique. Although fuzzy k-prototypes algorithms are well known for their efficiency in clustering numerical and categorical data, they are sensitive to the selection of initial cluster centers. In this paper, the impact of cluster center initialization on improving the pbkp_rediction accuracy of 2FA-kprototypes was analyzed and discussed using two cluster initialization techniques: centrality-based initialization and density-based initialization. The performance of 2FA-kprototypes using these two initialization techniques was evaluated and compared with that of 2FA-kprototypes using random initialization over four datasets: ISBSG, COCOMO81, USP05-FT, and USP05-RQ. The results showed an improvement in the performance of 2FA-kprototypes in terms of estimation accuracy when the all-in method is used.