An Analogy-Based Approach to Estimation of Software Development Effort Using Categorical Data

Abstract

Analogy-based software development effort estimation methods have proved to be a viable alternative to other conventional estimation methods since they mimic the human problem solving approach. However, they are limited by their inability to correctly handle categorical data. Therefore, we have proposed, in an earlier work, a new approach called fuzzy analogy which extends classical analogy by incorporating the fuzzy logic concept in the estimation process. The proposed approach may be applied only when the categorical values are derived from numerical data. This paper extends fuzzy analogy to deal with categorical values that are not derived from numerical data. To this aim, we used the fuzzy k-modes algorithm, a well-known clustering technique for large datasets containing categorical values. Thereafter, we evaluate the accuracy of fuzzy analogy construction-based on fuzzy k-modes using the ISBSG R8 dataset. This evaluation shows that our proposed approach leads to significant improvement in estimation accuracy.