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

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. (C) 1997 Elsevier Science Inc.