AI-Driven SRS Generation for a Pension Pro PlatformPension Tech
Our client, is a leading SaaS provider of pension administration solutions, serving public sector organizations. With decades of experience, they support pension boards across multiple jurisdictions with compliance-focused, mission-critical services.
They faced significant challenges in maintaining accurate and efficient documentation of functional requirements across complex JavaScript modules. With business logic dispersed across multiple files and no standardized SRS structure, teams struggled with time-intensive manual reviews, inconsistent documentation practices, and increased risk of missed validations. To overcome these inefficiencies and strengthen traceability between code and requirements, the client turned to an AI-powered approach to automate SRS extraction, structuring, and validation.
challenge
- Distributed Business Logic Across Multiple JS Files: Core operations such as New Member Creation, Record Loading, Updating,
and Deletion were distributed across multiple separate JS files. Developers
and business analysts faced a steep learning curve in tracing data flows and
validation rules scattered across the codebase. - Time-Intensive Manual Documentation: Generating an SRS required manual review of each JS file, interpretation of
logic, mapping of field-level validations, and cross-checking against
business rules. - Validation and Data Mapping Complexity:Operations included pre-populating member IDs, performing conditional
validations, loading dropdowns from lookup tables, and linking with multiple
data sources. Misinterpretation could lead to incorrect functional
requirements or missed edge cases in the SRS. - Lack of Standardized SRS Structure: Documentation formats varied across teams, making it harder to maintain a
consistent repository of requirement specifications for compliance and
audit purposes.
solution
- AI-Powered Requirements Extraction:The AI was given all multiple JS files and a simple natural language prompt to generate an SRS.
- The model analyzed:
- Field initialization logic in new.js
- Data validation and dropdown population rules in load.js
- Record lifecycle rules in delete.js and save.js
- It extracted functional requirements into clear submodules (New, Load, Save, Delete) without manual intervention.
- Automated SRS Structuring:The AI output followed a standardized 6-part SRS format:
- Introduction
- Functional Requirements (segmented by JS logic)
- Data Requirements (Actual Tables & Lookup Tables)
- Non-Functional Requirements (UX, performance, legacy system alignment)
- Error & Warning Handling
- Future Enhancements
- Embedded Contextual Intelligence:For each functional area, AI:
- Identified validation rules at the field level.
- Tagged dependencies on lookup tables vs. persistent data tables.
- Recommended non-functional enhancements for user experience alignment.
- Flexible Output for Collaboration: The AI-generated document was exportable in Word or PDF format.
- This enabled business analysts to review, annotate, and circulate for stakeholder sign-off without reformatting.
Outcomes
- 75% reduction in SRS preparation time (from ~12–16 hours to under 3 hours)
- 90% coverage of functional requirements extracted from all 4 JS modules
- Zero missed validations in the generated SRS compared to manual reviews
- 100% traceability established between code-level logic and documented requirements
- 50% fewer review cycles needed before QA kickoff
- On-demand regeneration of updated SRS within 5 minutes of code changes
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