In one of the world’s most complex financial technology environments, ensuring that every line of code functions flawlessly is a monumental challenge.
Behind the trading systems, risk engines, and client platforms that power global banking giant Goldman Sachs lies a vast internal infrastructure of software teams responsible for keeping the digital heartbeat of the bank running.
Within that ecosystem sits the Quality Assurance Engineering (QAE) organisation, a global group tasked with enabling engineering teams across the firm to deliver software that is robust, compliant, and resilient under pressure.
QAE’s remit is both technical and strategic: it defines testing standards, drives automation, and equips developers with the tools and frameworks to catch defects early. In a business where milliseconds and regulatory precision matter equally, the group’s mission is to make quality not just a checkpoint, but a culture.
That philosophy came into sharp focus when QAE confronted one of the most persistent challenges in enterprise software, the lack of consistent unit test coverage across large, legacy Java codebases. The team needed a way to scale test creation without slowing down delivery. To meet that goal, Goldman Sachs turned to AI.
Testing revamp
The bank’s engineers deployed Diffblue Cover, an artificial intelligence tool that automatically writes Java unit tests, as part of a broader strategy to modernise how testing is done.
“We decided to use Diffblue Cover because of the potential it offered for helping us meet our most ambitious code coverage targets, while also freeing up developers’ time for the work only they can do,” explained Matt Davey, who is the bank’s managing director, technology QAE & SDLC.
The results were immediate, Davey claimed. In one key backend module, code coverage jumped from 36 percent to 72 percent within 24 hours, an improvement that would have taken more than eight days of manual developer effort.
In another example, for a component with 15,000 lines of code, Diffblue produced over 3,000 unit tests overnight, roughly 180 times faster than human output.
By integrating Diffblue Cover into its workflows, the QAE team redefined how testing is approached across Goldman Sachs’ global engineering estate. Automation has allowed developers to focus on higher-value tasks such as feature delivery and system optimisation, while simultaneously improving quality.
“Diffblue Cover is enabling us to improve quality and build new software faster,” Davey said.
The impact went beyond speed. AI-generated tests uncovered unexpected edge cases and stability issues, strengthening the bank’s risk posture and reducing future incidents.
The generated tests were of such quality that they could be merged directly into the firm’s existing suite, with minimal review overhead.
Goldman Sachs now plans to expand its use of Diffblue Cover across additional applications and business lines, embedding it into standard QAE processes.
For QA and testing teams across banking and financial services, the firm’s experience illustrates how AI can move beyond hype to solve one of the industry’s oldest problems: maintaining high-quality code at scale.
As Davey put it, Goldman Sachs’ AI-assisted approach marks a shift in mindset, from reactive testing to proactive quality engineering. It is a transformation that signals not just faster software, but a more confident, resilient foundation for the digital bank of the future, he argued.
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