A replicated assessment and comparison of adaptation techniques for analogy-based effort estimation

Variants of adaptation techniques have been proposed in previous studies to improve the performance of analogy-based effort estimation. The results of these studies are often contradictory and cannot simply be generalized because there are many uncontrollable source of variations between adaptation studies. The study presented in this paper has been carried out in order to replicate the assessment and comparison of different adaptation techniques utilised in analogy-based software effort prediction. Empirical evaluation of variants of adaptation techniques with Jack-knifing procedure have been carried out. Seven datasets come from PROMISE data repository were used for benchmarking. The results are also investigated within the presence/absence of feature subset selection algorithm. The current study permitted us to discover that linear adjustment approaches are more accurate than nonlinear adjustment because of the nature of the employed datasets that have, in most cases, normality characteristics.