Comparative Study on Applicability of Four Software Size Estimation Models Based on Lines of Code

Early estimation of project size and completion time is essential for successful project planning and tracking. Multiple methods have been proposed to estimate software size and cost parameters. Suitability of the estimation methods depends on many factors like software application domain, product complexity, availability of historical data, team expertise etc. We present an empirical validation of four software size estimation methods based on Lines of Code and object-oriented paradigm. Line of Code (LOC) is still a commonly used software size measure. Despite the fact that software sizing is well recognized as an important problem for more than two decades, there is still much problem in existing methods. This paper proposes a method for estimating LOC for an information system from its conceptual data model through the use of multiple linear regression models. We have validated the method through collecting samples from both the industry and opensource systems published in literature. In this paper we report some experimental evaluations of metrics for OO models. Such metrics were obtained by adapting for OO models some metrics that ware originally conceived for OO code. According to the reported results, we sketch a methodological approach to the measurement of model size and other quantitative characteristics. A cross validation approach was adopted to build and evaluate linear and polynomial models where the independent variable was a traditional OO entity: classes, methods, association, inheritance, or a combination of them. Key-Words: Sizing Metric, Functional Size, Source Lines of Code, multiple linear regression model, Objectoriented systems, empirical validation