用多模态AI提升软件需求点估计算法精度
Multimodal Generative AI for Story Point Estimation in Software Development
- 融合文本、图像和类别数据，用BERT/CNN/XGBoost联合建模
- 简单需求估计准确率高，复杂类别受数据不均衡影响大
- 揭示严重程度等类别特征对估算的关键影响，适合敏捷开发团队
本研究探索多模态生成式AI在敏捷软件开发中故事点估算的应用。通过整合文本、图像和类别数据，采用BERT、CNN与XGBoost等先进模型，突破传统单模态估算方法的局限。结果表明，对于简单故事点具有较高准确性，而复杂类别因数据不平衡面临挑战。研究进一步分析了类别数据（尤其是严重程度）对估算过程的影响，强调其对模型性能的关键作用。成果凸显多模态数据融合在优化AI驱动项目管理中的变革潜力，为实现更精准、灵活且领域特定的AI能力铺平道路。此外，本文还指出了未来应对数据变异性、增强AI在敏捷方法中鲁棒性的方向。
原文摘要 · Abstract (English)
This research explores the application of Multimodal Generative AI to enhance story point estimation in Agile software development. By integrating text, image, and categorical data using advanced models like BERT, CNN, and XGBoost, our approach surpasses the limitations of traditional single-modal estimation methods. The results demonstrate strong accuracy for simpler story points, while also highlighting challenges in more complex categories due to data imbalance. This study further explores the impact of categorical data, particularly severity, on the estimation process, emphasizing its influence on model performance. Our findings emphasize the transformative potential of multimodal data integration in refining AI-driven project management, paving the way for more precise, adaptable, and domain-specific AI capabilities. Additionally, this work outlines future directions for addressing data variability and enhancing the robustness of AI in Agile methodologies.
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