Abstract
Analogy-based estimation has recently emerged as a promising technique and a viable alternative to other conventional estimation methods. One of the most important research areas for analogy-based cost estimation is how to predict the effort of software projects when they are described by mixed numerical and categorical data. To address this issue, we have proposed, in an earlier work, a new approach called fuzzy analogy combining the key features of fuzzy logic and analogy-based reasoning. However, fuzzy analogy may only be used when the possible values of the categorical attributes are derived from a numerical domain. The current study aims to extend our former approach to correctly handle categorical data. To this end, the fuzzy k-modes algorithm is used with two initialization techniques. The performance of the proposed approach was compared with that of classical analogy using the International Software Benchmarking Standards Group (ISBSG) dataset. The obtained results show significant improvement in estimation accuracy.
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