Analogy-X: Providing Statistical Inference to Analogy-Based Software Cost Estimation

Abstract Data-intensive analogy has been proposed as a means of software cost estimation as an alternative to other data intensive methods such as linear regression. Unfortunately, there are drawbacks to the method. There is no mechanism to assess its appropriateness for a specific dataset. In addition, heuristic algorithms are necessary to select the best set of variables and identify abnormal project cases. We introduce a solution to these problems based upon the use of the Mantel correlation randomization test called Analogy-X. We use the strength of correlation between the distance matrix of project features and the distance matrix of known effort values of the dataset. The method is demonstrated using the Desharnais dataset and two random datasets, showing (1) the use of Mantel's correlation to identify whether analogy is appropriate, (2) a stepwise procedure for feature selection, as well as (3) the use of a leverage statistic for sensitivity analysis that detects abnormal data points. Analogy-X, thus, provides a sound statistical basis for analogy, removes the need for heuristic search and greatly improves its algorithmic performance.