A methodology to attain site specificity and model simplicity in software development effort estimation
Girish Subramanian Harihara · 1991
The dissertation seeks to develop an improved method of Software Development Effort Estimation. Software development effort estimation helps in estimating the person-months for the software project and is useful in: (1) aiding the marketing function for making realistic quotes, (2) ensuring that cost estimates are realistic and reasonable profits are feasible, (3) answering the make or buy software decision, and (4) manpower and resource planning for software development. The dissertation addresses the problems of model complexity and portability encountered in current estimation models. The dissertation work develops a reduced dimensionality method to provide model simplicity and site specificity in effort estimation. This method is general and can be ported to various sites to yield models specific to these sites. Factor analysis is used to isolate the dimensions that underlie the independent variable space. These dimensions clarify the relationship among the variables in the independent variable space and assists the choice of a smaller set of site relevant variables. If these dimensions (by definition, few in number) can be used to generate an effort estimate comparable to the estimate derived from the variables from which these dimensions originate, model simplicity is achieved. As these dimensions are not correlated (or have low correlation), the problem of multicollinearity often present in current models is avoided. Site specificity is achieved by the process of choosing measures for these dimensions. Using a subset of possible options for arriving at these measures, a multivariate regression approach was used to generate models on a sizeable portion of the COCOMO data base. A testing mechanism is used to subject the models obtained from this method to the test criteria recommended in the literature. The accuracy of the estimates obtained from these models were compared to the estimates obtained from the COCOMO basic, intermediate and detailed models on Conte's criteria. The testing supports the merit of the reduced dimensionality method; the method outperforms COCOMO according to Conte's criteria. Further, discriminant analysis is used to predict the ability of the dimensionality reduction method to estimate a given project within 20% of the actual in either (over or under) direction.