Open AccessStochastic gradient boosting for predicting the maintenance effort of software‐intensive systems
Author(s) -
Cerón-Figueroa Sergio,
López-Martín Cuauhtémoc,
Yáñez-Márquez Cornelio
Publication year - 2020
Publication title -
iet software
Language(s) - English
Resource type - Journals
ISSN - 1751-8814
DOI - 10.1049/iet-sen.2018.5332
Subject(s) - computer science , gradient boosting , benchmarking , boosting (machine learning) , software maintenance , software , artificial neural network , software project management , machine learning , software engineering , data mining , software system , artificial intelligence , software construction , random forest , programming language , marketing , business
The maintenance of software‐intensive systems (SISs) must be undertaken to correct faults, improve the design, implement enhancements, adapt programmes such that different hardware, software, system features, and telecommunications facilities can be used, as well as to migrate legacy software. A lack of planning has been identified as one explanation for late and over budget software projects. An activity of planning is effort prediction. The goal of this study is to propose the application of a stochastic gradient boosting (SGB) model for predicting the SIS maintenance effort. We compare the SGB prediction accuracy with those obtained with statistical regression, neural network, support vector regression, decision trees, and association rules. We trained and tested the models with five SIS data sets selected from the International Software Benchmarking Standards Group Release 11. The SGB prediction accuracy was statistically better than the mentioned five models in the two larger data sets. We can conclude that a SGB can be applied to predict the maintenance effort of SISs coded in languages of the third generation and developed on either mainframes or multi‐platform. The predicted effort corresponds to the aggregate of efforts obtained from the project team, project management, and project administration.