Why Reverse Engineering Alone Isn’t Enough: The Role of AI in Requirements Engineering

Dipal Patel
September 18, 2025

Why This Matters

In Part 1: 3X Faster Documentation with AI Reverse-Engineering, we focused on the strategic need for AI-powered reverse engineering and its business impact. But the real unlock happens when AI-generated outputs are translated into well-documented business requirements that drive enhancements, modernization, and transformation projects.

This part is designed for Business Analysts (BAs), Program Managers, and CTOs who want to move beyond code-level discovery and focus on requirement engineering—the step that turns technical documentation into actionable business outcomes. Tools play a critical role in this journey, and so do practical tips on how to use them effectively.

1. The Bridge Between Code and Requirements

Reverse engineering is often misunderstood as a purely technical exercise. But without requirements engineering, the outputs—process flows, ERDs, dependency maps—are just artifacts. The real challenge is:

  • Translating technical logic into business-friendly requirements
  • Capturing gaps between actual system behavior and intended business processes
  • Creating a baseline of current state requirements that can guide enhancements or modernization

This is where Business Analysts and the right tooling ecosystem come together.

2. The Tool Landscape: What’s Out There

A growing set of platforms and frameworks support AI-driven reverse engineering. Some of the most impactful include:

  • CAST Highlight & CAST AIP – Strong in dependency mapping, portfolio risk analysis, and executive dashboards. Useful for large-scale modernization.
  • IBM Wazi Analyze – Tailored for COBOL and mainframe environments, widely used in BFSI and regulated industries.
  • Micro Focus Enterprise Analyzer – Legacy system analysis at scale, especially in government and insurance sectors.
  • Open-source options such as SrcML, Doxygen, and graph DB integrations with Neo4j – Lightweight and flexible, good for enterprises building custom pipelines.
  • Custom NLP & LLM Models – Increasingly popular for translating business rules into plain-English requirements.

These tools accelerate discovery, but their real value comes when paired with BA-driven requirements engineering.

3. Tips & Tricks: Getting More Value from Tools

Merely deploying tools isn’t enough. Enterprises maximize ROI when they:

  • Feed tool outputs into graph databases – CAST Highlight outputs can be ingested into Neo4j for richer visualization and dependency impact analysis.
  • Use NLP overlays – Pair reverse engineering outputs with LLMs to generate business-friendly requirement drafts.
  • Automate traceability – Integrate CAST or Micro Focus outputs directly into JIRA or Azure DevOps as Epics/Stories for better alignment.
  • Validate early with SMEs – Don’t wait until the end. Ensure outputs are vetted by domain experts within the first 2–3 weeks.
  • Continuous sync with CI/CD – Automate reverse engineering runs weekly so requirement documentation remains evergreen.
  • Prioritize high-value areas – Run tools on the 20% of systems driving 80% of business value first.

Practical example: One BFSI client used CAST Highlight for discovery, but the real breakthrough came when BAs imported outputs into JIRA. Suddenly, business requirements, test cases, and modernization sprints were all linked back to the codebase.

4. The Role of Business Analysts in Reverse Engineering

AI-driven tools generate a wealth of information, but it’s raw. BAs refine and contextualize this into structured requirements. Their responsibilities include:

  • Requirements Elicitation: Using AI outputs as a baseline to engage stakeholders.
  • Requirements Documentation: Converting diagrams into functional and non-functional requirements.
  • Gap Analysis: Identifying differences between actual code behavior and expected business processes.
  • Requirements Traceability: Linking technical artifacts to requirements and test cases.

BAs ensure AI doesn’t just produce artifacts—but creates business clarity.

5. Documenting Requirements: A Structured Approach

AI outputs need to be transformed into requirement engineering deliverables. The process includes:

  • Current State Documentation – Capture auto-generated workflows, ERDs, and dependency maps.
  • Requirement Writing – Convert findings into clear, testable requirements using IEEE or BABOK standards.
  • Use Case Modeling – Translate technical logic into “As a user, I need…” statements.
  • Prioritization – Apply frameworks like MoSCoW to focus on the most business-critical requirements.
  • Traceability – Build a Requirements Traceability Matrix (RTM) linking AI outputs → requirements → test cases → modernization goals.

6. Enhancements and New Requirement Creation

Reverse engineering doesn’t just document what exists—it reveals opportunities:

  • Optimization: Eliminate redundant workflows documented by AI.
  • Compliance: Align undocumented logic with regulations and create new compliance requirements.
  • Modernization: Define new requirements such as API enablement, scalability, or cloud-readiness.
  • Business Value: Derive requirements for automation, real-time reporting, or improved user experiences.

Case Example: In one insurance engagement, undocumented underwriting rules were discovered. BAs converted these into a new centralized rules engine requirement, cutting premium errors and improving revenue accuracy by 12%.

7. Case-in-Point Examples

  • BFSI: Legacy to Modern Pension Platform – V2Solutions collaborated with a financial services client to modernize legacy pension administration systems. BAs created API-first modernization requirements that optimized performance and scalability. Client Outcome: 30% faster pension processing and improved API-driven integrations.
  • Healthcare: Legacy to Cloud – A 20-year-old healthcare EMR was migrated to the cloud. Reverse engineering uncovered undocumented workflows, which BAs converted into compliance requirements that ensured HIPAA alignment and reduced audit risk. Client Outcome: 50% reduction in audit prep time and improved compliance confidence.
  • Manufacturing: Reverse engineering uncovered batch job inefficiencies. BAs documented new performance requirements, reducing reporting cycles from 12 hours to 2 hours. Client Outcome: Reporting accelerated by 80%, enabling near real-time decision-making.
  • Gaming: Elevating Gaming with Multi-Platform Microservices – Legacy gaming architecture was transformed into scalable microservices. Analysts turned dependency maps into requirements for cross-platform support and performance optimization. Client Outcome: 10X user scalability with seamless cross-platform gaming experiences.
  • IT Services: Seamless Transition – Windows Workflow Foundation to Modern Tech – Reverse engineering outputs were structured into modernization requirements, providing a clear roadmap for workflow transformation and platform performance. Client Outcome: Reduced transition risks and faster time-to-value for modernization.

8. Common Challenges & How to Overcome Them

  • Overwhelming AI outputs: Prioritize requirements tied to measurable business outcomes.
  • Stakeholder alignment issues: Use diagrams as neutral artifacts to build consensus.
  • Documentation fatigue: Automate formatting of requirements and traceability matrices.
  • BA skill gaps: Train analysts to interpret AI-generated artifacts effectively.

9. Strategic Outcomes of Requirements-Driven Reverse Engineering

When AI tooling and requirements engineering converge, enterprises achieve:

  • Modernization Alignment: Requirements ensure technical updates deliver measurable value.
  • Audit Readiness: Regulatory requirements become embedded in documentation.
  • Innovation Enablement: Enhancements are identified early and documented.
  • Business-IT Synergy: Requirements bridge the gap between technical teams and executives.

According to Gartner, enterprises that combine AI tooling with structured requirements engineering are 2X more likely to succeed in modernization initiatives.

FAQs

For COBOL-heavy portfolios, IBM Wazi is best. For mixed stacks, CAST Highlight offers broader coverage.

By using BA frameworks—IEEE templates, RTMs, and stakeholder validation workshops.

Some NLP models attempt this, but BA validation is always required.

Expect $150K–$500K for enterprise-scale reverse engineering projects depending on tool licensing and system complexity.

Yes. Outputs can be converted into Epics/Stories in JIRA or Azure DevOps.

Closing Thoughts

Reverse engineering without requirements engineering is just documentation. The real value emerges when you translate AI outputs into actionable requirements that fuel enhancements, compliance, and modernization.

Next Step: Schedule a Requirements Engineering Workshop with V2Solutions. We’ll show you how AI outputs can be converted into structured, business-ready requirements that accelerate transformation.

➡️ If you missed it, read Part 1: 3X Faster Documentation with AI Reverse-Engineering for the strategic foundation.