Multimodal Generative AI for Story Point Estimation

This research explores Multimodal Generative AI for Agile story point estimation, integrating text, images, and categorical data with BERT, CNN, and XGBoost to enhance project planning. While effective for simpler estimates, challenges with complex cases due to data imbalance highlight the need for tailored strategies. Findings underscore multimodal AI's impact on Agile decision-making and suggest future improvements in data handling and model robustness.

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Publication Details

Published
2025-05-05
DOI
https://doi.org/10.1109/cai64502.2025.00120
Citations
1
Primary Topic
Human Motion and Animation
Type
article
Field-Weighted Citation Impact
1.21
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article

Multimodal Generative AI for Story Point Estimation

Peter Sandborn, Mohammad Rubyet Islam
1 citations
Human Motion and Animation
1.21
article

Multimodal Generative AI for Story Point Estimation

Peter Sandborn, Mohammad Rubyet Islam
article en
1 citations

Abstract

This research explores Multimodal Generative AI for Agile story point estimation, integrating text, images, and categorical data with BERT, CNN, and XGBoost to enhance project planning. While effective for simpler estimates, challenges with complex cases due to data imbalance highlight the need for tailored strategies. Findings underscore multimodal AI's impact on Agile decision-making and suggest future improvements in data handling and model robustness.

George Mason University (US), University of Maryland, College Park (US)
Openalex Percentile: Top 19%
Human Motion and Animation
1.21
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