AI-Driven C++ to Java Modernization Case Study

Problem Statement

An Online Payments Major undertook the C++ elimination initiative because the language had become a concentrated course of security risk, engineering drag and talent exposure across it's payment platform.

As a result, as a strategic initiative, Online Payments Major are set to retire the legacy C++ programs and services in favor of modern, standardized Java-based solutions

Challenges

Elevated security exposure from memory-unsafe code

Unmaintained framework meant CVEs couldn’t be reliably patched without manual efforts on aging codebases. Running critical payment infrastructure of these systems posed a security risk that was not sustainable in the long term.

Resilience

Loss of resources with both domain knowledge and legacy programming language posed a risk whereby the company started to lose its ability to make functional changes to legacy applications. Knowledge concentration in aging systems created unacceptable Single point of failure (SPOF) on services processing high volume traffic.

A shrinking talent pool

Usage of legacy application written in C++ has stayed flat around 20% of developers for years, with candidates more selective and slower to hire, while the customer’s existing first-class language (Go, Java) drew from a deeper, cheaper talent pool.

Slow build/compile cycles and migration cost

C++ build and test cycles were noticeably slower than modern alternatives, thereby impacting developer’s throughput.

Solution

The Stratacent team did a deep analysis of the challenges Online Payments Major faced and proposed a multi-faceted strategic approach.

Multi-level Modernization Strategy

Rather than treating C++ elimination as a single rewrite, the recommendation was to approach it as a portfolio-level modernization strategy build on language modernization proved before being scaled.

Memory- by-safe-design

Shift in strategy from addressing individual vulnerabilities by patching, the approach moved to memory-safe language (Java Raptor) which removed an entire category of exploitable defects by construction.

AI-leveraged modernization tooling

To compress migration timelines and reduce the risk in re-platforming business-critical logic, the modernization initiative incorporated AI-assisted tooling across the modernization lifecycle: LLM-assisted code comprehension, AI-assisted translation and refactoring to accelerate the porting of logic to Java, and automated test-case generation.

AI-assisted migration

AI-powered conversion tool to accelerate migration at scale. This solution enabled automated conversion, not a start-from-scratch effort. Furthermore, it streamlined significant portion workload conversion, which in turn allowed engineering team to focus on verification and behavioral validation. This changed the cost calculus for teams assessing whether migration was feasible within program timelines.

Value Delivered

Vulnerability Remediation

Migration of over 300 clients leveraging encryption function of legacy system to Java based application. This results in avoidance of security-related costs by removing an entire-bug-class rather than patching instances. Industry benchmark puts payback for this kind of memory-safe modernization at roughly 18-24 months.

Reclaim engineering capacity

Free up engineering capacity for Online Payments Major (time spent on fighting legacy complexity) for other strategic initiatives

Lower hiring/retention cost:

Staffing against a liquid Java pool market instead of a shrinking and pricier C++ one. Additionally, shifting critical systems onto to Java converted a hiring liability into an hiring advantage.

Speed to market:

AI-leveraged tooling enabled migration speed and code quality to compound. Preliminary findings (Industry-wide) benchmark LLM-assisted migration completing edits twice as fast compared to manual approach and reducing migration timelines nearly by half.

Accelerating Payment Platform Modernization with AI-Driven C++ to Java Transformation.