A LinkedIn post from Qodo highlights growing complexity in software testing and code review workflows as AI-generated code increases. The post contrasts conventional “shift-left testing” with earlier, in-context review of changes, emphasizing the need to examine code before it reaches formal pull request stages.
According to the post, Google’s DORA 2025 research is cited to suggest that teams with high AI adoption ship 154% larger pull requests, face 91% longer review times, and experience 9% more bugs. The post also notes that while pull request volumes continue to rise, reviewer capacity is not increasing at the same pace.
The content suggests an emerging operational bottleneck in development pipelines that may create demand for tools and processes that bring review and testing closer to code creation. For investors, this focus on early-stage review and test practices points to an addressable pain point in the DevOps and developer tooling markets where Qodo appears to be positioning its offerings.
By featuring guidance from Filip Hric on what effective pre–pull request review should cover, the post implies an educational and thought-leadership approach aimed at teams adopting AI in their workflows. This positioning may help Qodo build credibility with engineering organizations, potentially supporting customer acquisition and product adoption in a segment facing measurable efficiency and quality challenges.