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.

中文翻译：

---

随机梯度增强可预测软件密集型系统的维护工作

必须进行软件密集型系统（SIS）的维护，以纠正错误，改进设计，实施增强功能，修改程序，以便可以使用不同的硬件，软件，系统功能和电信设施，以及迁移旧版软件。 。缺乏计划已被认为是逾期和预算过高的软件项目的一种解释。计划活动是努力预测。这项研究的目的是建议使用随机梯度增强（SGB）模型来预测SIS维护工作量。我们将SGB预测准确性与通过统计回归，神经网络，支持向量回归，决策树和关联规则获得的SGB预测准确性进行比较。我们使用从国际软件基准标准组第11版中选择的五个SIS数据集对模型进行了训练和测试，在两个较大的数据集中，SGB的预测准确性在统计上优于上述五个模型。我们可以得出结论，可以将SGB应用于预测以第三代语言编码并在大型机或多平台上开发的SIS的维护工作。预计的工作量对应于从项目团队，项目管理和项目管理获得的工作量的总和。我们可以得出结论，可以将SGB应用于预测以第三代语言编码并在大型机或多平台上开发的SIS的维护工作。预计的工作量对应于从项目团队，项目管理和项目管理获得的工作量的总和。我们可以得出结论，可以将SGB应用于预测以第三代语言编码并在大型机或多平台上开发的SIS的维护工作。预计的工作量对应于从项目团队，项目管理和项目管理获得的工作量的总和。