  One of the most important objectives of software engineering community has
been the increase of useful models that beneficially explain the development of
life cycle and precisely calculate the effort of software cost estimation. In
analogy concept, there is deficiency in handling the datasets containing
categorical variables though there are innumerable methods to estimate the
cost. Due to the nature of software engineering domain, generally project
attributes are often measured in terms of linguistic values such as very low,
low, high and very high. The imprecise nature of such value represents the
uncertainty and vagueness in their elucidation. However, there is no efficient
method that can directly deal with the categorical variables and tolerate such
imprecision and uncertainty without taking the classical intervals and numeric
value approaches. In this paper, a new approach for optimization based on fuzzy
logic, linguistic quantifiers and analogy based reasoning is proposed to
improve the performance of the effort in software project when they are
described in either numerical or categorical data. The performance of this
proposed method exemplifies a pragmatic validation based on the historical NASA
dataset. The results were analyzed using the prediction criterion and indicates
that the proposed method can produce more explainable results than other
machine learning methods.
