Beyond the Grid: The Rise of Architectural Digital Spaces

Challenge

Unit test suites are a key part of effective software development and continuous integration, but creating them for legacy code is resource-intensive. Goldman Sachs aimed to efficiently boost legacy code coverage and allow engineering teams to refocus their efforts on the development of innovative, business-critical new features.

Solution

Goldman Sachs has a long history as a technology leader among global banks. The latest advance is the company’s use of Diffblue Cover, an AI-powered tool that enables the engineering teams to improve code quality and efficiency through the automatic generation of unit tests.

Results

Since starting the legacy modernization program using Diffblue Cover, the engineering teams have increased test coverage for the first batch of applications from 36% to 72% in less than 10% of the time it would take to do manually.*

For some applications, this result was achievable in hours or days rather than years. Higher code coverage has also provided greater confidence in application stability when adding new code, improving the speed at which the engineering teams can deliver business value.

The Legacy Code Challenge

Unit tests—tests that confirm the functionality of individual units of code—are a small but critical part of the software development lifecycle. These fast, lightweight tests make it possible to track the connections between units of code so developers can write and refactor with confidence. When an organization has an automated suite of unit tests that covers all existing code, any new code a developer adds is immediately checked against the entire codebase, and the developer is alerted if their modifications cause any issues or breaking changes in the code’s behavior.

Goldman Sachs’ QAE Team

Goldman Sachs has a long history as a technology leader among global banks, and the Goldman Sachs Quality Assurance Engineering (QAE) team is responsible for empowering the company’s engineers to proactively deliver quality software and services. The processes put in place by the QAE team enable the early identification of quality gaps with low-touch controls across Goldman Sachs technology.

The QAE team has been working towards reaching industry best coverage levels. However, given the company’s large legacy estate and the volume of unit tests required to increase the average level of code coverage, the team had also been looking for ways to efficiently bolster productivity. Artificial intelligence (AI) was a natural avenue to explore.

Proposed Solution: Diffblue Cover

While working towards the goal of bringing every application in Goldman Sachs to higher levels of code coverage, the QAE team landed on Diffblue Cover, a tool that automatically and intelligently writes unit tests for Java applications using AI for code. One of Diffblue Cover’s primary benefits is its unique ability to rapidly generate a test suite for legacy codebases.

“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,” says Matt Davey, Managing Director, Technology QAE & SDLC. “Diffblue Cover is enabling us to improve quality and build new software faster.”

Results: Doubled Code Coverage in a Fraction of Results: Doubled Code Coverage in a Fraction of the Time

Diffblue Cover has been implemented on various applications within Goldman Sachs; for each software product, a suite of high-quality tests has been generated in less than one day. For one module within an important backend system, existing unit test coverage was boosted from 36% to 72% in less than 24 hours. Creating the same number of unit tests manually would have taken more than eight days of developer time,* compared to three-quarters of a workday with Diffblue—a time saving of more than 90%. Diffblue Cover also picked up on edge cases in other applications that could have led to customer-impacting incidents.

Another back-end application has fifteen thousand lines of code. Diffblue Cover created over three thousand tests overnight. Compared to the time it would have taken to write these 3,211 unit tests manually, Diffblue Cover was more than 180 times faster.*