The cost of adopting new technology is rarely analyzed and discussed, while it is vital for many software companies worldwide. Thus, it is crucial to consider Return On Investment (ROI) when performing data analytics. Decisions on "How much analytics is needed"? are hard to answer. ROI could guide decision support on the What?, How?, and How Much? Analytics for a given problem. This work details a comprehensive tool that provides conventional and advanced ML approaches for demonstration using requirements dependency extraction and their ROI analysis as use case. Utilizing advanced ML techniques such as Active Learning, Transfer Learning and primitive Large language model: BERT (Bidirectional Encoder Representations from Transformers) as its various components for automating dependency extraction, the tool outcomes demonstrate a mechanism to compute the ROI of ML algorithms to present a clear picture of trade-offs between the cost and benefits of a technology investment.

翻译：采用新技术的成本很少被分析和讨论，而这对于全球众多软件公司而言至关重要。因此，在进行数据分析时，考虑投资回报率（ROI）至关重要。“需要多少分析量？”这类决策难以回答。对于给定的问题，ROI可以指导关于“分析什么？”、“如何分析？”以及“分析多少？”的决策支持。本研究详细介绍了一款综合性工具，该工具提供传统和先进的机器学习方法进行演示，并以需求依赖性提取及其ROI分析作为用例。该工具利用主动学习、迁移学习以及原始大型语言模型BERT（基于Transformer的双向编码器表示）等先进机器学习技术作为其自动化依赖性提取的各个组件，其输出结果展示了一种计算机器学习算法ROI的机制，从而清晰呈现技术投资成本与收益之间的权衡关系。