Interpretable Software Maintenance and Support Effort Prediction Using Machine Learning
Software maintenance and support efforts consume a significant amount of the software project budget to operate the software system in its expected quality. Manually estimating the total hours required for this phase can be very time-consuming, and often differs from the actual cost that is incurred...
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| Vydané v: | Proceedings (IEEE/ACM International Conference on Software Engineering Companion. Online) s. 288 - 289 |
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| Hlavní autori: | , |
| Médium: | Konferenčný príspevok.. |
| Jazyk: | English |
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ACM
14.04.2024
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| ISSN: | 2574-1934 |
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| Abstract | Software maintenance and support efforts consume a significant amount of the software project budget to operate the software system in its expected quality. Manually estimating the total hours required for this phase can be very time-consuming, and often differs from the actual cost that is incurred. The automation of these estimation processes can be implemented with the aid of machine learning algorithms. The maintenance and support effort prediction models need to be explainable so that project managers can understand which features contributed to the model outcome. This study contributes to the development of the maintenance and support effort prediction model using various tree-based re-gression machine-learning techniques from cross-company project information. The developed models were explained using the state-of-the-art model agnostic technique SHapley Additive Explanations (SHAP) to understand the significance of features from the developed model. This study concluded that staff size, application size, and number of defects are major contributors to the maintenance and support effort prediction models. |
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| AbstractList | Software maintenance and support efforts consume a significant amount of the software project budget to operate the software system in its expected quality. Manually estimating the total hours required for this phase can be very time-consuming, and often differs from the actual cost that is incurred. The automation of these estimation processes can be implemented with the aid of machine learning algorithms. The maintenance and support effort prediction models need to be explainable so that project managers can understand which features contributed to the model outcome. This study contributes to the development of the maintenance and support effort prediction model using various tree-based re-gression machine-learning techniques from cross-company project information. The developed models were explained using the state-of-the-art model agnostic technique SHapley Additive Explanations (SHAP) to understand the significance of features from the developed model. This study concluded that staff size, application size, and number of defects are major contributors to the maintenance and support effort prediction models. |
| Author | Capretz, Luiz Fernando Haldar, Susmita |
| Author_xml | – sequence: 1 givenname: Susmita surname: Haldar fullname: Haldar, Susmita email: shaldar@fanshawec.ca organization: School of Information Technology, Fanshawe College,London,Ontario,Canada – sequence: 2 givenname: Luiz Fernando surname: Capretz fullname: Capretz, Luiz Fernando email: lcapretz@uwo.ca organization: Western University,Department of Electrical and Computer Engineering,London,Ontario,Canada |
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| Snippet | Software maintenance and support efforts consume a significant amount of the software project budget to operate the software system in its expected quality.... |
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| SubjectTerms | Automation Costs Estimation Explainable machine learning models Machine learning Machine learning algorithms Maintenance and support effort prediction Model agnostic interpretation Predictive models Software maintenance |
| Title | Interpretable Software Maintenance and Support Effort Prediction Using Machine Learning |
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