Statistical Approach for Predicting Factors of Mood Method for Object Oriented

TL;DR

This paper employs a statistical linear regression approach to predict key object-oriented software metrics like LOC, NOC, NOM, and NOA, aiding early design decisions and complexity management.

Contribution

It introduces a novel application of linear regression to predict object-oriented design metrics, enhancing early software quality assessment.

Findings

Successful prediction of software measurements using linear regression

Early design insights can be gained to reduce complexity

Improved decision-making in object-oriented software development

Abstract

Object oriented design is becoming more popular in software development and object oriented design metrics which is an essential part of software environment. The main goal in this paper is to predict factors of MOOD method for OO using a statistical approach. Therefore, linear regression model is used to find the relationship between factors of MOOD method and their influences on OO software measurements. Fortunately, through this process a prediction could be made for the line of code (LOC), number of classes (NOC), number of methods (NOM), and number of attributes (NOA). These measurements permit designers to access the software early in process, making changes that will reduce complexity and improve the continuing capability of the design.