Class Complexity Metric to Predict Understandability

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Kumar Rajnish 1,*

1. Department of Information Technology, Birla Institute of Technology, Mesra, Ranchi, India

* Corresponding author.


Received: 14 Nov. 2013 / Revised: 11 Dec. 2013 / Accepted: 6 Jan. 2014 / Published: 8 Feb. 2014

Index Terms

Complexity, Metrics, Object-Oriented, Classes, Understandability, Methods, Instance variables


This paper presents a new class complexity metric of an Object-Oriented (OO) program which is used to predict the understandability of classes. The propose complexity metric is evaluated theoretically against Weyuker's properties to analyze the nature of metric and empirically evaluated against three small projects developed by Post Graduate (PG)/Under Graduate (UG) teams. Least Square Regression Analysis technique is performed to arrive at the result and find correlation coefficient of propose metric with the Degree of Understandability. The result indicates that the propose metric is a good predictor of understandability of classes. JHAWK TOOL (Java Code Metrics Tool) were used to evaluate the parameters values involved in propose metric and for analyzing the results of projects, Matlab6.1 and IBM SPSS software were used.

Cite This Paper

Kumar Rajnish, "Class Complexity Metric to Predict Understandability", International Journal of Information Engineering and Electronic Business(IJIEEB), vol.6, no.1, pp.69-76, 2014. DOI:10.5815/ijieeb.2014.01.08


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