Abstract
Abstract In this study, empirically investigates the relationship of existing class level object-oriented metrics with a quality parameter i.e., maintainability. Here, different subset of Object-Oriented software metrics have been considered to provide requisite input data to design the models for predicting maintainability using Neuro-Genetic algorithm (hybrid approach of neural network and genetic algorithm). This technique is applied to estimate maintainability on two different case studies such as Quality Evaluation System (QUES) and User Interface System (UIMS). The performance parameters of this technique are evaluated based on the basis of Mean absolute error (MAE), Mean Absolute Relative Error (MARE), Root Mean Square Error (RMSE), and Standard Error of the Mean (SEM). The results reported that the identified subset metrics demonstrated an improved maintainability prediction with higher accuracy.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
