Attribute Weighted Class Complexity: A New Metric for Measuring Cognitive Complexity of OO Systems

In general, class complexity is measured based on any one of these factors such as Line of Codes (LOC), Functional points (FP), Number of Methods (NOM), Number of Attributes (NOA) and so on. There are several new techniques, methods and metrics with the different factors that are to be developed by the researchers for calculating the complexity of the class in Object Oriented (OO) software. Earlier, Arockiam et.al has proposed a new complexity measure namely Extended Weighted Class Complexity (EWCC) which is an extension of Weighted Class Complexity which is proposed by Mishra et.al. EWCC is the sum of cognitive weights of attributes and methods of the class and that of the classes derived. In EWCC, a cognitive weight of each attribute is considered to be 1. The main problem in EWCC metric is that, every attribute holds the same value but in general, cognitive load in understanding the different types of attributes cannot be the same. So here, we are proposing a new metric namely Attribute Weighted Class Complexity (AWCC). In AWCC, the cognitive weights have to be assigned for the attributes which are derived from the effort needed to understand their data types. The proposed metric has been proved to be a better measure of complexity of class with attributes through the case studies and experiments

Authors:

• Dr. L. Arockiam
• A. Aloysius

Keywords:

• Software Complexity
• Attribute Weighted Class Complexity
• Weighted Class Complexity
• Data Type

References:

[1] Arockiam. L, Aloysius. A,Charles selvaraj. J "Extended Weighted Class
Complexity: A new measure of software complexity for objected oriented systems", Proceedings of International Conference on Semantic E-business and Enterprise computing (SEEC), 2009, pp. 77-80.
[2] Charles Selvaraj. J, Aloysius. A, and Arockiam. L , "A Comparision of
Proposed Cognitive weights for control structures and object oriented
programming languages", Proceedings of International Conference on Advanced Computing (ICAC09), 2009, pp. 380-385.
[3] Sanjay Misra and k. Ibrahim Akman, "Weighted Class Complexity: A
Measure of Complexity for Object Oriented System," Journal of
Information Science and Engineering 24, 2008, pp. 1689-1708.
[4] Mc Quillan. J. A and Power. J. F, "On the application of software
metrics to UML model," Lecture Notes in Computer Science, Vol. 4364,
2007, pp. 217-226.
[5] Ranjeeth. S, Ramu Naidoo "An Investigation Into The Relationship
Between The Level Of Cognitive Maturity And The Types Of Errors
Made By Students In A Computer Programming" College Teaching
Methods & Style Journal-Second Quarter, 2007, pp. 31-40.
[6] Rajnish. K, Bhattacherjee. V," A New Metric for Class Inheritance
Hierarchy: An Illustration", proceedings of National Conference on
Emerging Principles and Practices of Computer Science & Information
Technology", GNDEC, Ludhiana, 2006, pp. 321-325.
[7] Wang. Y and Shao. J, "A new measure of software complexity based on
cognitive Weights." IEEE Canadian Journal of Electrical and Computer
Engineering, 2003, pp. 69-74.
[8] Basili. VR, Briand. L. C, Melo. WL, "A validation of object oriented
design metrics as quality indicators", Technical report,University of
Maryland, Department of Computer Science,1995, pp. 1-24.
[9] Chidamber. S. R and Kemerer. C. F, "A Metric Suite for Object-
Oriented Design", IEEE Trans. on Software Engineering, 1994, 476-
493.
[10] Harrison. R, Counsell. SJ, Nithi. RV, "An evaluation of the MOOD set
of Object-oriented software metrics", IEEE Trans.On Software
Engineering, 1998, pp. 491- 496.
[11] Wang. Y, "On Cognitive Informatics." IEEE International Conference
on Cognitive Informatics, 2002, pp. 69-74.