Customer expectations for speed and reliability have upended the software development life cycle. Whereas developers used to develop a product and send it to quality assurance engineers for testing before widespread release, customers now demand new versions and features on a quicker timeline. In an era with little available time to run QA in the traditional development process, what’s the alternative?
Fortunately, modern automated infrastructure provides a solution. Infrastructure-as-code (IaC) toolchains that deliver continuous observability with AIOps let SRE teams overlook the entire process, from the first piece of code through the software product’s public release.
This merger of the traditional roles of QA and monitoring is enabling quicker speeds within the delivery cycle, a development that improves the customer experience but also ignites business innovation.
The Benefits of Speed in the SDLC
Observability delivers the data necessary to eliminate traditional QA, but teams still need help. With intelligent observability — where you apply AI to the data — teams can better focus their efforts and analyze information much more quickly.
Finding and fixing software issues after deployment unnecessarily stresses SRE teams. But through observability with AIOps, DevOps practitioners and SREs monitor from early development through daily performance. QA is omnipresent. Teams can identify root causes of issues instantly, and AI surfaces the most actionable steps to quickly resolve impacts on the infrastructure.
The DevOps team used to leave QA engineers with the tedious and time-consuming task of pinpointing issues amid a sea of data, creating a bottleneck within the development process. The continuous learning and intelligent collaboration approach, on the other hand, merges traditional QA and monitoring and yields a CI/CD pipeline that actually works.
Integrating observability with AI into the development cycle allows teams to spot change almost instantly. If the platform starts acting oddly after its release, SREs and DevOps practitioners can jump in immediately without the need to wait for a QA team. If the issue is where a change in performance is expected, AI and machine learning often still can reduce what’s needed from your teams. If, for instance, you’re monitoring with an adaptive thresholding algorithm, you can let the algorithm retrain and learn the new behavior instead of relying on the DevOps team to communicate the expected change in performance to the QA team.
As QA shifts left – as evidenced by the growth of software development engineers in test (SDET) – it’s natural that monitoring and assurance capabilities should shift left, too.
A Principled Approach to QA
The merger of quality assurance and monitoring throughout the software development cycle follows the three guiding principles behind DevOps practices.
- Flow/system thinking. The approach eliminates workflow silos and instead creates a holistic view of the development process. The continuous oversight negates typical quality hazards, such as the handoff that can occur despite a known issue or the optimization for local efficiency only.
- Amplifying feedback loops. A merging of QA and monitoring eliminates the need to loop feedback through multiple teams with various processes because teams are given consistent feedback throughout software development and delivery.
- A culture of continual experimentation and learning. DevOps teams, no longer in a position to just pass software down the line to QA, will build constant improvement into their processes as they learn from bugs along the way. This also provides more opportunity to take risks and experiment. The approach is a more holistic product cycle versus quick development.
Improving the Q in QA
Removing QA as a singular step in the software development process initially may seem like removing quality assurance itself. But QA is actually heightened by integrating observability with AIOps into the development cycle.
Observability with AIOps allows teams to see into systems as they are being built but also allows them to gain actionable insights to resolve issues on the overall structure. Actionable insights empower the merging of traditional QA and monitoring to drive a whole new speed of delivery, which yields better customer experiences and gives businesses the ability to quickly launch competitive, innovative services.
