Dataset Quality Assessment: An extension for analogy based effort estimation

TL;DR

This paper introduces a statistical method based on Kendall's rank correlation to evaluate and improve dataset quality for analogy-based effort estimation in software engineering, addressing categorical attribute handling.

Contribution

It presents a novel data quality assessment method that extends EBA by enabling attribute-specific evaluation and abnormal observation detection without dataset partitioning.

Findings

Effective dataset quality evaluation for EBA

Ability to handle categorical attributes individually

Identification of abnormal observations

Abstract

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.