"Time to clean up human slop": Why AI now reviews code better than your teammate.

• Run a full test suite on the code, and verify that “coverage” (i.e., all lines of code that exist do in fact execute in the test) is meaningful, not just present.
• Manually verify the behavior end-to-end before opening a pull request for the code written thus far.
• Open the pull request and let the AI review tool run. Loop its comments back to the coding AI, decide which to act on and which to dismiss, and iterate.
• Merge, then monitor. Check error rates and metrics. Own the outcome.

“That process is more rigorous than waiting 48 hours for an LGTM,”  Tamir says. “Plus, it puts accountability exactly with the person who understands the problem. The uncomfortable truth is that much code review is just theater. It creates the appearance of rigor without reliably delivering it.”

It's a question of trust

Given the presence of these kinds of AI-code review tools today and the propositions being made here, we may ultimately get down to questioning the fabric of software engineering team structure and the management approach that oversees it.

This brings trust into the picture. Tamir suggests that if leadership doesn’t trust software engineers to review their own work responsibly, “that’s a hiring problem, not a process problem” in real terms.

“Trust in high-performing teams is built through outcomes: shipping features that work, owning failures and fixing them fast, proactively sharing knowledge, and including colleagues in decisions. Those behaviors create a track record that a peer review approval queue does not,” he concludes.

Teams considering embracing some or all of these techniques may want to start with a low-risk internal software tool, or perhaps a greenfield service or a non-customer-facing system. In concert with this approach (and while the team measures its success, deployment frequency, rollback rate, etc.), they can reserve synchronous human collaboration for higher-stakes decisions.

This way, team collaboration might really start to matter and make a difference, and we could shift from LGTM to looks really good to me.

Community created roadmaps, articles, resources and journeys for

developers to help you choose your path and grow in your career.