Quality aspects for component-based systems: A metrics based approach
Article first published online: 20 JAN 2012
Copyright © 2012 John Wiley & Sons, Ltd.
Software: Practice and Experience
Volume 42, Issue 12, pages 1531–1548, December 2012
How to Cite
Kumar, V., Sharma, A., Kumar, R. and Grover, P.S. (2012), Quality aspects for component-based systems: A metrics based approach. Softw: Pract. Exper., 42: 1531–1548. doi: 10.1002/spe.1153
- Issue published online: 20 NOV 2012
- Article first published online: 20 JAN 2012
- Manuscript Accepted: 29 OCT 2011
- Manuscript Received: 29 NOV 2010
In component-based development, software systems are built by assembling components already developed and prepared for integration. To estimate the quality of components, complexity, reusability, dependability, and maintainability are the key aspects. The quality of an individual component influences the quality of the overall system. Therefore, there is a strong need to select the best quality component, both from functional and nonfunctional aspects. The present paper produces a critical analysis of metrics for various quality aspects for components and component-based systems. These aspects include four main quality factors: complexity, dependency, reusability, and maintainability. A systematic study is applied to find as much literature as possible. A total of 49 papers were found suitable after a defined search criteria. The analysis provided in this paper has a different objective as we focused on efficiency and practical ability of the proposed approach in the selected papers. The various key attributes from these two are defined. Each paper is evaluated based on the various key parameters viz. metrics definition, implementation technique, validation, usability, data source, comparative analysis, practicability, and extendibility. The paper critically examines various quality aspects and their metrics for component-based systems. In some papers, authors have also compared the results with other techniques. For characteristics like complexity and dependency, most of the proposed metrics are analytical. Soft computing and evolutionary approaches are either not being used or much less explored so far for these aspects, which may be the future concern for the researchers. In addition, hybrid approaches like neuro-fuzzy, neuro-genetic, etc., may also be examined for evaluation of these aspects. However, to conclude that one particular technique is better than others may not be appropriate. It may be true for one characteristic by considering different set of inputs and dataset but may not be true for the same with different inputs. The intension in the proposed work is to give a score for each metric proposed by the researchers based on the selected parameters, but certainly not to criticize any research contribution by authors. Copyright © 2012 John Wiley & Sons, Ltd.