Prediction of Software Maintenance Effort of Object Oriented Metrics Based Commercial Systems (original) (raw)

The software systems really advanced and seize with problems on their maintenance. The software maintenance work is presently one in every one of the foremost tough, time-consuming, expensive and costly tasks in the software development life cycle. It’s invariably been a vital issue for software project developers. Therefore, it is worthwhile to develop an object oriented system with easy maintenance at design phases. This analysis concentrates the development of a method based on the data mining techniques as K-means and Hierarchical clustering are implemented in MATLAB package on two commercial systems are UIMS (User Interface Management System) QUES (Quality Evaluation System). The maintenance effort is measured by the number of lines changed (addition or a deletion) per class which are already pre defined classes of UIMS and QUES. It is ascertained that the algorithms will be able to decide the cluster with Easy, Medium, and High conditions of maintainable classes of similarity based on object oriented metrics. This paper is most beneficial for the software maker and maintainers to take the necessary steps at design level to design of maintainable object oriented software.

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