Statistical Approach for Predicting Factors of Mood Method for Object Oriented
This work addresses software designers by providing a statistical tool for early complexity prediction in object-oriented development, but it appears incremental as it applies standard regression to an existing method.
The paper tackles the problem of predicting key object-oriented software metrics (LOC, NOC, NOM, NOA) by using a linear regression model to analyze factors of the MOOD method, enabling early design assessment to reduce complexity and improve maintainability.
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.