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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Proceedings (IEEE/ACM International Conference on Software Engineering Companion. Online) s. 288 - 289
Hlavní autoři: Haldar, Susmita, Capretz, Luiz Fernando
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: ACM 14.04.2024
Témata:
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
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
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