A hybrid model for prediction of software effort based on team size

Abstract

Abstract Most of the software development organisations frequently use an appreciable amount of resources to estimate the effort in the beginning of the development process. In most of the cases, inaccurate estimates tend to wastage of these resources. Very few generalised models have been found in the literature. These models have been developed using the prototype dataset of the organisation. The project management team of an organisation tries to predict the effort needed for the development of software using various mathematical techniques. These techniques are mostly based on statistical methods (viz. simple linear regression (SLR), multi linear regression, support vector machine, cascade correlation neural network (CCNN) etc.) and some probability‐based models. They use historical data of similar projects. The work presented in this article envisages the use of Support Vector Regression (SVR) and constructive cost model (COCOMO), where SVR can be used for both linear and non‐linear models and COCOMO can be used as a regression model. The proposed hybrid model has been tested on the International Software Benchmarking Standards Group dataset. The data has been grouped according to the size of man power. It has been found that the proposed model yields better results than the SVR or SLR for each group of data in general.

Keywords

• neural nets
• probability
• project management
• regression analysis
• software cost estimation
• software development management