Browsing by Author "Pinidiyaarachchi, A. J"

Now showing 1 - 4 of 4

Embargo
Analysis and enhancements of a cognitive based complexity measure
(IEEE, 2017-06-25) De Silva, D. I; Kodagoda, N; Kodituwakku, S. R; Pinidiyaarachchi, A. J
As stated by Tom DeMacro, something that cannot be measured is uncontrollable. Thus, a number of metrics have been developed to measure the complexity associated with software by considering various aspects such as size, control flow and data flow between modules, cognitive informatics etc. Amongst these aspects, cognitive informatics is recognized as a promising aspect in measuring software complexity. Thus, majority of the complexity metrics that were proposed after the introduction of cognitive informatics have been proposed mainly based on the cognitive aspect. Amongst them, Chhillar and Bhasins' weighted composite complexity measure is one of the few metrics that had attempted to measure the complexity of a program by considering more than three or more complexity factors. After a thorough analysis, in a previous study, the authors identified that the weighted composite complexity measure could be further improved by considering more complexity factors. This paper extends the previous study to identify the most appropriate factors that could be considered by the weighted composite complexity measure. Using the opinions of the industry experts, the authors were able to discover that compound conditional statements, threads and recursion could also be considered by the weighted composite complexity measure. Accordingly, the weighted composite complexity measure was enhanced to capture the complexities that arise due to those factors. The paper also includes a demonstration of the complexity calculation method of the improved weighted composite complexity measure with the use of three sample java programs, which were written by incorporating the above mentioned factors. In addition, an application of the weighted composite complexity measure to the same programs are also given in the paper, to illustrate the changes in complexity values of the two measures.
Open Access
Enhancements to an OO Metric: CB Measure.
(Journal of Software, 2018-01-01) De Silva, D. I; Kodituwakku, S. R; Pinidiyaarachchi, A. J; Kodagoda, N
Due to the wide usage of the object-oriented paradigm as a development paradigm many researches have proposed metrics to measure the complexity of object-oriented programs. The proposed object-oriented metrics can be divided into two categories based on the main aspect they have considered: metrics based on object-oriented aspects and metrics based on the cognitive aspects. Majority of the metrics which belong to the latter category have relied on a maximum of three complexity factors to derive the complexity of a program. CB measure is one of the few metrics that has considered four or more complexity factors to measure the complexity associated with a software program. However, there exists some other factors that could be considered by the CB measure to make it a more practically applicable measure. Such factors were proposed by the authors in a previous study. This paper demonstrates how those factors can be incorporated to the CB measure. In addition, it validates the practical applicability of the modified CB measure.
Embargo
Limitations of an object-oriented metric: Weighted complexity measure
(IEEE, 2015-09-23) De Silva, D. I; Kodagoda, N; Kodituwakku, S. R; Pinidiyaarachchi, A. J
Many computer science practitioners and software developers believes that the complexity of a program could be controlled more effectively by using object-oriented programming concepts. In addition to controlling complexity, the object-oriented approach allows faster development, reduction in costs, higher quality, easier maintenance, increased scalability, better information structures, and increased adaptability. As such, more and more programs are written using the object-oriented programming approach rather than using the traditional functional approach. This demand has spurred the provision for a number of object-oriented metrics. Out of them, Chidamber and Kemerers' metrics suite is one of the most prominent object-oriented metrics that has been proposed. It has been widely validated and has been accepted as a useful predictor of object-oriented design complexity. But it does not consider the complexities that occur due to factors such as the nesting level and type of control structures, and the size of the program. Thus, Chhillar and Bhasins' introduced the weighted complexity measure to address these issues. It is the only metric which considers the complexities that occur due to inheritance level of statements, nesting level and type of control structures, and the size of the program. However, weighted complexity measure also has some limitations. This paper attempts to draw the readers' attention to those limitations, with the hope that it will be further improved by addressing them.
Embargo
Limitations of an object-oriented metric: Weighted complexity measure
(IEEE, 2015-09-23) De Silva, D. I; Kodagoda, N; Kodituwakku, S. R; Pinidiyaarachchi, A. J
Many computer science practitioners and software developers believes that the complexity of a program could be controlled more effectively by using object-oriented programming concepts. In addition to controlling complexity, the object-oriented approach allows faster development, reduction in costs, higher quality, easier maintenance, increased scalability, better information structures, and increased adaptability. As such, more and more programs are written using the object-oriented programming approach rather than using the traditional functional approach. This demand has spurred the provision for a number of object-oriented metrics. Out of them, Chidamber and Kemerers' metrics suite is one of the most prominent object-oriented metrics that has been proposed. It has been widely validated and has been accepted as a useful predictor of object-oriented design complexity. But it does not consider the complexities that occur due to factors such as the nesting level and type of control structures, and the size of the program. Thus, Chhillar and Bhasins' introduced the weighted complexity measure to address these issues. It is the only metric which considers the complexities that occur due to inheritance level of statements, nesting level and type of control structures, and the size of the program. However, weighted complexity measure also has some limitations. This paper attempts to draw the readers' attention to those limitations, with the hope that it will be further improved by addressing them.