VetsEZ Middleware Development

Overview

Development stage

In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.

High-impact designation

Not high-impactSource: c) Not high-impact

Impact justification

Not reported

Start date

Not reported

Withheld from public reporting

Not reported

Mission

Topic area

Information Technology

Operational functions

Software development assistance Derived by keyword rules; see methodology

Problem the AI is intended to solve

The HDM middleware environment supports large-scale, secure health data transport across VA systems, but much of its underlying codebase is legacy (e.g., MUMPS, Java, .NET Framework) and complex, with inconsistent documentation and a heavy reliance on senior SMEs. This leads to: Ongoing technical debt as well-functioning but aging middleware components require careful, phased modernization; Longer onboarding times when new developers need to interpret complex, high-value legacy code without comprehensive documentation; Resource dependencies where mid-tier developers require more direct SME involvement for certain modernization and refactoring tasks; Variation in code patterns that can affect consistency in security, performance, and maintainability across platforms; Slower adoption of modern architectures such as containerized microservices and FHIR integrations. How AI Solves It: Explain legacy code in plain language, creating complete documentation for knowledge preservation and onboarding; Refactor code for maintainability, performance, and security, reducing technical debt; Generate standardized, efficient middleware code aligned with VA modernization goals; Produce AI-augmented unit tests to detect defects earlier (“shift-left” testing) and increase coverage; All outputs will still undergo human review, VA security scanning, and governance to ensure compliance with Federal Information Security Management Act (FISMA) High standards and VA TRM policy.

Expected benefits

For the VA’s Mission: Faster code modernization – AI-assisted refactoring and generation will help transition middleware components from older platforms (MUMPS, Java, .NET Framework) to secure, containerized microservices. Improved developer efficiency – By automating repetitive coding & documentation tasks, developers can focus more on high-value modernization work and less on manual rework. Knowledge preservation – AI-generated code explanations and documentation reduce the risk of key-person dependency and improve continuity across teams. Higher code quality – AI-augmented unit tests and standardized patterns will strengthen maintainability and alignment with FISMA High requirements. Accelerated delivery cycles – With AI enabling mid-tier developers to work more independently, releases can move through the DevSecOps pipeline faster while maintaining quality and compliance. For the General Public / Veterans: Improved reliability of VA systems – Modernized, better-tested middleware helps ensure health and benefits data flows securely and consistently. Faster implementation of new services – Reduced development cycle times allow Veterans to benefit sooner from new integrations, such as Fast Healthcare Interoperability Resources (FHIR)-based interoperability with community care providers. Sustained continuity of operations – Documentation and knowledge capture ensure that mission-critical systems can be maintained and improved even as staff transitions occur.

System outputs

AI System Outputs The system will produce the following outputs to support HDM middleware modernization: Code Explanations & Documentation Plain-language summaries of complex legacy code (e.g., MUMPS, Java, .NET Framework) to aid in developer understanding and onboarding. Structured documentation suitable for inclusion in VA knowledge repositories. Refactored Code Updated, cleaner, and more maintainable versions of existing middleware code that preserve original functionality while improving performance, security, and alignment with modern coding standards. New Code Segments Efficient, standardized code snippets or modules to support integration tasks, modernization efforts, and migration to containerized microservices. AI-Generated Unit Test Scaffolds Automated creation of baseline unit test templates that improve early defect detection, expand test coverage, and support “shift-left” testing practices. Code Review Suggestions Recommendations for improving existing or newly generated code, focusing on maintainability, security, and architectural alignment.

Technology

AI classification

Generative AISource: Generative AI: AI that generates new or synthetic content (e.g., images, videos, audio, text, code).

System name(s)

Not reported

Custom-developed code

Not reported

Public source code

Not reported

Sourcing

How it was built

Not reported

Vendor (as reported)

Not reported

Vendors (standardized)

None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII

Not reported

Privacy Impact Assessment

Not reported

Authorization to Operate

Not reported

Demographic features

Not reported

Training and evaluation data

Not reported

Federal Data Catalog

Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Potential impacts and how they were identified

Not reported