  One of the significant objectives of software engineering community is to use
effective and useful models for precise calculation of effort in software cost
estimation. The existing techniques cannot handle the dataset having
categorical variables efficiently including the commonly used analogy method.
Also, the project attributes of cost estimation are measured in terms of
linguistic values whose imprecision leads to confusion and ambiguity while
explaining the process. There are no definite set of models which can
efficiently handle the dataset having categorical variables and endure the
major hindrances such as imprecision and uncertainty without taking the
classical intervals and numeric value approaches. In this paper, a new approach
based on fuzzy logic, linguistic quantifiers and analogy based reasoning is
proposed to enhance the performance of the effort estimation in software
projects dealing with numerical and categorical data. The performance of this
proposed method illustrates that there is a realistic validation of the results
while using historical heterogeneous dataset. The results were analyzed using
the Mean Magnitude Relative Error (MMRE) and indicates that the proposed method
can produce more explicable results than the methods which are in vogue.
