Software Size Measurement: Bridging Research and Practice

Hacaloğlu, Tuna; Ünlü, Hüseyin; Yıldız, Ali; Demirörs, Onur

doi:10.1109/ms.2024.3358079

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Other1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Application of a Size Measurement Standard for Data Warehouse Projects

Ünlü,

Yürüm,

Yıldız

et al. 2024

Softw Pract Exp

View full text Add to dashboard Cite

MethodologyIn this research, we conducted a case study to establish a foundation for size measurement and effort estimation in DWH projects. We first applied a productivity‐based estimation approach using linear regression with the ISBSG repository to assist organizations without historical data. We then evaluated various machine learning algorithms to improve estimation accuracy. Finally, we tested a combined model that integrates both approaches for estimating effort in external projects.ResultsUsing the ISBSG dataset, linear regression models based on productivity achieved a Mean Magnitude of Relative Error (MMRE) of 0.285. Machine learning algorithms improved accuracy by 22.81%, reducing the MMRE to 0.220. The final model, applied to external projects, yielded MRE values between 0.010 and 0.245.ConclusionThe ISBSG repository is a valuable resource for effort estimation in DWH projects. Combining productivity‐based estimation with machine learning enhances accuracy and predictive performance, making it a more reliable approach than traditional models.

show abstract