Jellyfish and Sonar Team Up to Provide Better Visibility into Code Quality, Coverage and Vulnerability Metrics

At Jellyfish, we’re keenly aware of the broad industry shift towards AI adoption. We also know that, based on dozens of conversations with our customers and extensive surveys, that this shift has brought, in addition to enhanced productivity and speed, a whole host of new concerns around code quality, reliability and developer trust. Engineering leaders often ask what it means to be “good at AI,” how to control for quality and manage tech debt all while balancing demands to go even faster.

To help address these concerns, I am excited to share that Jellyfish is teaming up with Sonar to bring our joint customers in-depth data on code quality and security via SonarQube – all within the Jellyfish platform.

For all of the benefits of AI, this year’s DORA report further highlights the increased instability that can come with AI generated code. The report’s authors note: “AI adoption not only fails to fix instability, it is currently associated with increasing instability.”

Instability and code quality are big drivers of tech debt, which can slow a team down, negatively impact team sentiment and erode the team’s ability to deliver innovation, thereby diminishing growth and customer satisfaction. Sonar calls this the “Danger Zone” where AI code is a driver for engineering dysfunction.

Source: Sonar

Given the abrupt change AI has brought to our field, it’s not surprising then that code quality data has been the number one most requested new integration category among Jellyfish customers and prospects.

It’s very useful for our teams to be able to see the failure rate of SonarQube quality gates in Jellyfish. It’s interesting because it can be directly related to the ability of our teams to deliver effectively.

– Nicolas Martenet, Quality Lead at Nexthink

Spot Trends and Act Instantly

Together, Jellyfish and SonarQube provide actionable insights into code quality, code coverage, and vulnerability metrics across teams and over time, allowing engineering managers to spot trends and act instantly.

We’re excited to partner with Jellyfish to bring our joint customers better visibility across the SDLC,” said Harry Wang, Chief Growth Officer at Sonar. “While the rise of AI-assisted development has sped up development cycles, it has also introduced the need for added verification and security. Together with Jellyfish, we are helping engineering leaders accurately measure the benefits of reliable, secure code.

With Jellyfish and SonarQube you can:

• Access real-time data on the latest code quality metrics along with ready-to-use dashboards displaying SonarQube data directly inside the Jellyfish Metrics Explorer
• Monitor code quality and coverage trends alongside delivery metrics, so you can understand how productivity changes are impacting software maintainability
• Track vulnerabilities and security issues by team so you can ensure risks are visible at the same level as velocity
• Spot repos where quality is slipping early so you can provide support before it becomes a bigger issue
• Connect quality metrics with investment data, so you can understand the cost and impact of paying down tech debt
• Filter SonarQube data by several attributes such as Team, Project, Repository, Pull Request, Branch, Job Title, Level, Location, and Quality Gate Status providing more precise information that can help you better spot areas of concern

Jellyfish + SonarQube Cloud

This integration is available now to all Jellyfish customers. If you’re ready to get better visibility into code quality, coverage and vulnerability metrics with Jellyfish and SonarQube, sign up for a demo or reach out to your customer success manager today.