Size Classification for Software Maintenance Projects Through a Simplified Minimalist Machine Learning Algorithm

Abstract

In the software engineering field, the size of projects is used as explanatory variable for predicting the effort, duration, defects, costs, or risks of a project. Thus, the software project size has been considered the most influential factor. A systematic mapping study on the use of categorical data in software prediction concludes that the use of categorical data as explanatory variable in prediction models is an important issue because in the first phases of the software development process, the information is expressed in a categorical manner rather than numerical; however, at date, software size has mostly been used in its quantitative form rather than in its categorical representation. Because the software enhancement maintenance has the highest impact on business, our purpose is to classify software enhancement projects from their size by applying a new model termed simplified minimalist machine learning (S-MML) algorithm, which belongs to the minimalist machine learning (MML) paradigm. S-MML reduces five attributes commonly used for sizing a software project to only one. The performance of the S-MML is compared to those obtained from three classifiers. Seven public datasets of projects obtained from an international public repository of software projects were used to train and test the classifiers. Results showed that the S-MML had a better f -measure than the other three classifiers for all of the datasets at 95% confidence. We can conclude that the S-MML can be applied to classify the size of software enhancement projects.