Open AccessOptimized neuro-PSO-based software maintainability prediction using relief features selection method
Author(s) -
N. Baskar,
C. Chandrasekar
Publication year - 2019
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v15.i3.pp1517-1526
Subject(s) - maintainability , software sizing , computer science , software development , verification and validation , software , reliability engineering , software construction , software metric , software quality , software engineering , data mining , machine learning , engineering , operating system , operations management
The recent development in software engineering reveals the importance of software maintenance during the time of software development that is becoming more important in software development environment and software metrics, which are very essential for measuring the maintainability of software, software complexity, estimating size, quality and project efforts. There are various approaches through which one can estimate the software cost and predict on various kinds of deliverable items. This paper aims at developing an optimized Neuro-PSO-based software maintainability prediction model by applying the dimensionality reduction using relief feature selection method for identifying the optimal feature subsets in order to increase the accuracy and reduce the time complexity of the prediction model. The simulation result proves the performance of the proposed model which will be more beneficial for the software developers in predicting the maintenance of the software in advance.