The Future of Code Reviews: Trends to Watch in 2025 and Beyond

The year 2025 will redefine the future of code reviews, marking a reinvention in engineering practices.

If you’ve been in the software development space, you’ve likely noticed a shift. The question is no longer if AI will change how we work but how far will AI take this.

I’ve spent more hours than I can count poring over pull requests, untangling messy logic, and arguing over architecture decisions. If there’s one thing I’ve learned, it’s that this space never stays still. Tools like Google’s Jules and OpenAI’s CriticGPT are already shaking things up, pushing us to rethink what "coding" and "code reviews" really mean.

So, where are we headed? 

Let’s look at the trends reshaping code reviews in 2025—and why it’s going to be an exciting (and maybe a little bumpy) ride.

#Trend 1: Meaningful Metrics in Code Reviews 

Thoughtfulness is becoming the new currency in code reviews.

We’ve always known that the depth of a review matters more than its speed. But now, engineering teams are finally starting to act on it. 

Metrics like review turnaround time and defect rates still have their place, but they’re being joined by deeper, more nuanced questions: How many reviewers are meaningfully engaged? Is the feedback clear and actionable? Are insights from reviews being shared and applied across the engineering team?

Sure, a review might take a little longer—but if it helps uncover a hidden architectural flaw or sparks a discussion that shifts the engineering team’s entire approach, isn’t that worth the time?

Here’s the real test: Can your codebase evolve as your product grows? Fixing a bug is great, but if the team doesn’t understand why it happened, how long until the next one crops up? Thoughtful reviews don’t just solve problems—they prevent future ones and, more importantly, make your team stronger.

#Trend 2: The Role of Google’s Jules in Redefining Code Reviews

Google’s Jules is something to look forward to in 2025. 

Source: Google for Developers

It can fix buggy code, draft pull requests, and even map out complex tasks step by step. But here’s where things get interesting: automation doesn’t just fix problems—it exposes them.

Jules isn’t just cleaning up code; it’s showing us where our processes are broken. If you keep fixing the same bug over and over, is the real problem the code, or is it something deeper—like the way the system was designed in the first place? AI like Jules forces us to confront these inefficiencies we might otherwise ignore.

And then there’s the bigger question: if Jules handles the small stuff, are we ready to focus on the bigger challenges—like scaling our systems or building something truly innovative? Or could we, ironically, end up leaning so heavily on AI that we rely on it to solve problems we haven’t addressed at their core?

Jules might feel like a safety net, but it’s also a mirror, reflecting the gaps in how we build and think about software.

#Trend 3: Open AI’s CriticGPT: Can It Solve the Accuracy Challenges in Code Reviews?

CriticGPT is OpenAI’s response to one of AI’s biggest flaws: it’s not always right. Studies show that over 52% of ChatGPT’s code outputs contain inaccuracies—a statistic that’s both staggering and humbling. 

This is where CriticGPT comes to make a difference.

Source: CriticGPT

But what happens when CriticGPT calls out the humans? Picture this—you submit a pull request, feeling pretty confident, only for CriticGPT to point out logic gaps that even the senior devs missed. That’s not just humbling; it’s unsettling. Suddenly, it’s not just about whether the code is right—it’s about whether the team trusts its own instincts.

The challenge isn’t just using CriticGPT effectively; it’s figuring out how to balance AI feedback with human judgment. Can engineering teams learn to navigate this dynamic without second-guessing every decision they make? That’s the real test.

#Trend 4: Code Reviews as a Learning Platform

This isn’t something I’ve seen happening yet, but it feels like a natural evolution—and maybe even inevitable.

Imagine this: a junior developer submits a pull request with performance bottlenecks. Instead of simply rejecting it or fixing it quietly, AI-powered tools step in with tailored feedback. They could point out the exact issue, link to relevant documentation, or even suggest alternative approaches. Suddenly, the review isn’t just about fixing code—it’s about levelling up the person who wrote it.

This is where I see the potential for a big shift. As AI takes over the “what’s wrong” part of the review process, human reviewers will have the bandwidth to focus on the “why” and “how.”

Where Code Reviews Go From Here

We’re standing at the edge of a shift that’s bigger than just tools or processes.

The question isn’t whether code reviews will change; it’s how we’ll rise to the challenge of this AI-driven practices and reinvention. The future is full of opportunities to build not just better software, but better engineering teams.

And honestly? I can’t wait to see where we go from here.