New Webinar: Modernising Without Destabilising: How Bread Financial Is Building Confidence Through Change

Learn more

New webinar with Bread Financial

Learn more
Contact us

Blogs

A Guide to Performance Engineering in Continuous Integration

<span id="hs_cos_wrapper_name" class="hs_cos_wrapper hs_cos_wrapper_meta_field hs_cos_wrapper_type_text" style="" data-hs-cos-general-type="meta_field" data-hs-cos-type="text" >A Guide to Performance Engineering in Continuous Integration</span>

Date 29 June 2026

Author Team Capacitas

Continuous Integration is designed to ensure quicker delivery of changes to production. But we still need to ensure these changes meet perform to expectation. How can we deliver performance assurance in the fast moving world of CI ?

There are 4 key ingredients:

  1. Automation
  2. Risk-driven approach
  3. Performance engineering strategy
  4. Smart test analysis

1. Automation

In a CI environment, speed and accuracy are some of the biggest challenges. You need to keep up with the increased level of change, but ensure that you are not creating noise. Automation of test execution and test analysis can give you this edge.

Discover how to increase software delivery velocity without impacting  performance, download Agile Performance: How to Move Fast and Not Break Things

2. Risk-driven approach

A risk-driven approach ensures that you only concentrate on the sources of performance risk and can help you to keep up with the velocity of change. Not all changes need to be tested. This goes hand in hand with automation as automating the testing process should free up the time to spend on assessing risk. There are 6 key sources of performance risk for which each change should be assessed and appropriate mitigation actions designed:

6 pillars of software performance

3. Performance engineering strategy

Define a strategy for testing in continuous integration. This could mean mitigating medium/low risks through gradual rollouts, monitoring in production, modelling or production testing. This video describes the strategy used at Arcadia

 

4. Smart test analysis

Pattern matching is a technique the should speed up the traditionally lengthiest part of the performance engineering process; analysis. By using these techniques and defining what “good” looks like, we can automate the process of finding “bad”:

smart analysis

The next step is then to take that one step further with smart analysis, and using machine learning to gradually perfect the pattern matching process, reducing the frequency of false positive results.

Agile Performance: How to move fast and not break things

Team Capacitas
About the author

Team Capacitas

Capacitas is a cloud and AI value partner. We translate rapid technological change into enduring commercial advantage by converting every unit of compute into enterprise value.

FinOps and AI: Building the Financial Discipline for the Next Wave of Enterprise Intelligence

AI FinOps represents an evolution rather than a replacement of traditional FinOps. It extends the model into a domain where financial, technical, and product decisions are tightly interconnected.

Read insight

Confidence Under Load: How We Verified AKS Readiness for Peak

How Capacitas verified AKS readiness for peak demand by validating workload performance, autoscaling, cluster capacity, monitoring, and incident response.

Read insight

Building Cloud Resilience: Lessons from the AWS Outage

Learning from the Latest Outage. Events like this week’s AWS disruption highlight one clear truth: resilience must be designed, not assumed.

Read insight

Bringing Order to Chaos: A Practical Guide to Chaos Testing in the Cloud

In today’s cloud-native environments, resilience is not optional—it’s critical. Chaos testing has emerged as a key practice for validating system behaviour under failure conditions.

Read insight