Value of ranked voting methods for estimation by analogy

One long-standing issue in estimation by analogy (EBA) is finding closest analogies. Prior studies revealed that existing similarity measures are easily influenced by extreme values and irrelevant features. Instead of identifying closest projects based on the aggregated similarity degrees, the authors propose to use ranked voting methods that rank projects per feature, and then aggregate those ranks over all features using voting count rules. The project(s) with highest score will be the winners and form new estimate for the target project. This also enables us to automatically come up with the preferred number of analogies for each target project, since the winner set may contain more than a single winner. Empirical evaluation with Jack-knifing procedure has been carried out, in which nine datasets come from two repositories (PROMISE and ISBSG) were used for benchmarking. The proposed models are compared with some well known estimation methods: regular K-based EBA, stepwise regression, ordinary least-square regression and categorical regression tree. The performance figures of the proposed models were promising. The use of voting methods present some useful advantages: (i) saving time in finding appropriate K number of analogies for each individual project, (ii) no need for project pruning and (iii) no data standardisation are required.