  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.
