Software project estimation using smooth curve methods and variable selection and regularization methods using a wedge-shape form database

Context: The impact of an excellent estimation in planning, budgeting, and control, makes the estimation activities an essential element for the software project success. Several estimation techniques have been developed during the last seven decades. Traditional regression-based is the most often estimation method used in the literature. The generation of models needs a reference database, which is usually a wedge-shaped dataset when real projects are considered. The use of regression-based estimation techniques provides low accuracy with this type of database. Objective: Evaluate and provide an alternative to the general practice of using regression-based models, looking if smooth curve methods and variable selection and regularization methods provide better reliability of the estimations based on the wedge-shaped form databases. Method: A previous study used a reference database with a wedge-shaped form to build a regression-based estimating model. This paper utilizes smooth curve methods and variable selection and regularization methods to build estimation models, providing an alternative to linear regression models. Results: The results show the improvement in the estimation results when smooth curve methods and variable selection and regularization methods are used against regression-based models when wedge-shaped form databases are considered. For example, GAM with all the variables show that the R-squared is for Effort: 0.6864 and for Cost: 0.7581; the MMRE is for Effort: 0.1095 and for Cost: 0.0578. The results for the GAM with LASSO show that the R-squared is for Effort: 0.6836 and for Cost: 0.7519; the MMRE is for Effort: 0.1105 and for Cost: 0.0585. In comparison to the R-squared is for Effort: 0.6790 and for Cost: 0.7540; the MMRE is for Effort: 0.1107 and for Cost: 0.0582 while using MLR.