  Objective: This paper investigates the potential of ensemble learning for
variants of adjustment methods used in analogy-based effort estimation. The
number k of analogies to be used is also investigated. Method We perform a
large scale comparison study where many ensembles constructed from n out of 40
possible valid variants of adjustment methods are applied to eight datasets.
The performance of each method was evaluated based on standardized accuracy and
effect size. Results: The results have been subjected to statistical
significance testing, and show reasonable significant improvements on the
predictive performance where ensemble methods are applied. Conclusion: Our
conclusions suggest that ensembles of adjustment methods can work well and
achieve good performance, even though they are not always superior to single
methods. We also recommend constructing ensembles from only linear adjustment
methods, as they have shown better performance and were frequently ranked
higher.
