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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Podrobná bibliografia
Vydané v:Proceedings (IEEE/ACM International Conference on Software Engineering Companion. Online) s. 288 - 289
Hlavní autori: Haldar, Susmita, Capretz, Luiz Fernando
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: ACM 14.04.2024
Predmet:
Automation
Costs
Estimation
Explainable machine learning models
Machine learning
Machine learning algorithms
Maintenance and support effort prediction
Model agnostic interpretation
Predictive models
Software maintenance
ISSN:2574-1934
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Popis
Shrnutí: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.
ISSN:2574-1934
DOI:10.1145/3639478.3643069