Estimating software development effort using fuzzy clustering-based analogy

During the past decades, many studies have been carried out in an attempt to build accurate software development effort estimation techniques. However, none of the techniques proposed has proven to be successful at predicting software effort in all circumstances. Among these techniques, analogy-based estimation has gained significant popularity within software engineering community because of its outstanding performance and ability to mimic the human problem solving approach. One of the challenges facing analogy-based effort estimation is how to predict effort when software projects are described by a mixture of continuous and categorical features. To address this issue, the present study proposes an improvement of our former 2FA-kprototypes technique referred to as 2FA-cmeans. 2FA-cmeans uses a clustering technique, called general fuzzy c-means, which is a generalization of the fuzzy c-means clustering technique to cluster objects with mixed attributes. The performance of 2FA-cmeans was evaluated and compared with that of our former 2FA-kprototypes technique as well as classical analogy over six datasets that are quite diverse and have different sizes. Empirical results showed that 2FA-cmeans outperforms the two other analogy techniques using both all-in and jackknife evaluation methods. This was also confirmed by the win-tie-loss statistics and the Scott-Knott test.