A comparison of software effort estimation techniques: Using function points with neural networks, case-based reasoning and regression models

Abstract

Estimating software development effort remains a complex problem attracting considerable research attention. Improving the estimation techniques available to project managers would facilitate more effective control of time and budgets in software development. This paper reviews a research study comparing three estimation techniques using function points as an estimate of system size. The models considered are based on regression analysis, artificial neural networks and case-based reasoning. Although regression models performed poorly on the data set of 299 projects, both artificial neural networks and case-based reasoning appeared to have value for software development effort estimation models. Case-based reasoning in particular is appealing because of its similarity to expert judgment approaches and for its potential as an expert assistant in support of human judgment.