  Estimation by Analogy (EBA) is an increasingly active research method in the
area of software engineering. The fundamental assumption of this method is that
the similar projects in terms of attribute values will also be similar in terms
of effort values. It is well recognized that the quality of software datasets
has a considerable impact on the reliability and accuracy of such method.
Therefore, if the software dataset does not satisfy the aforementioned
assumption then it is not rather useful for EBA method. This paper presents a
new method based on Kendall's row-wise rank correlation that enables data
quality evaluation and providing a data preprocessing stage for EBA. The
proposed method provides sound statistical basis and justification for the
process of data quality evaluation. Unlike Analogy-X, our method has the
ability to deal with categorical attributes individually without the need for
partitioning the dataset. Experimental results showed that the proposed method
could form a useful extension for EBA as it enables: dataset quality
evaluation, attribute selection and identifying abnormal observations.
