Large organizations respond to numerous volumes of Request for Proposals (RFPs) annually. The process of responding to RFPs is largely a manual and time-, effort-, and intellect-intensive endeavor, and vulnerable to errors. To bridge these gaps, we present RFPAnaFit, a tool designed for RFP Analysis, Fitment evaluation, and automated response generation. RFPAnaFit provides an automated solution for generating responses to RFPs. RFP typically comprises a set of questions that outline the requirements for various capabilities that need to be addressed. It leverages product capability documents and uses multiple techniques to assess the fitment of RFP requirements against product capabilities and generate RFP response document. We have tested RFPAnaFit on a real-world, large-sized product with two RFPs, demonstrating its effectiveness and providing valuable insights. While the findings are shared within the context of an industry-specific product, we believe that researchers and practitioners will find the lessons learned from this approach applicable to other domains as well.

Typically, business applications have complex and extensive functionality. Often, rules are scattered throughout the application documentation and code, making it challenging to modify them. Because business rules change more frequently, it is preferable to externalize the rules and move them outside the application. The requirements specification can serve as a valuable source for extracting and classifying rules that can further aid in assigning the rules to the appropriate teams based on their areas of expertise and externalizing the rules in the implementation. We introduce RClassify, which extracts and classifies rules from requirements specifications spread across several NL documents into eight different business rule classes. RClassify combines NLP and ML-based classification approaches to improve classification accuracy. We discuss the implementation of this approach in three real-world large-size and complex products, demonstrating its effectiveness, experience, and lessons learned. While the findings are presented in the specific context of three industry products, we believe that researchers, practitioners, and tool vendors will find the insights and experiences gained from this approach applicable in other contexts.