Jaya Algorithm and Artificial Neural Network Based Approach for Object- Oriented Software Quality Analysis

This paper develops a technique by using Jaya algorithm and feed-forward neural network to determine the quality of object-oriented software by using Chidamber & Kemerer (CK) along with Li & Henry metrics. The technique basically focuses on the maintainability factor of software quality which in turn depends upon the software complexity. The software complexity is directly proportional to the number of changes done per class which is determined by the technique. The analysis has been done on UIMS (User Interface Management System) and QUES (Quality Evaluation System) datasets by using the mean absolute error as the analysis parameter. The reduction in the mean absolute error as compared to the existing state of art techniques along with the individual component of proposed technique proves the significance of the technique.