Eliminate These 8 Bad Habits for More Effective Peer Code Reviews

A good peer code review technique has been proven to increase software quality. Here are some tips to prevent harmful practices that might render your code reviews useless.

Introduction to Peer Code Review

Peer code review is one of those practices everyone agrees is valuable and almost no one does well. It’s not for lack of good intentions—it’s mostly bad habits that have accumulated over years of calendar-driven development cycles, vague policies, and the general human tendency to avoid awkward conversations about someone else’s code.

The good news is that fixing peer review is mostly a matter of stopping doing the wrong things.

Automated testing—unit testing, API testing, performance testing, static analysis, and the rest—covers the measurable, repeatable stuff.

Peer review is where human judgment gets to work on what tools can’t: design problems, maintainability, the quiet alarm that goes off when you read code and think "this is going to hurt us later." That only happens when reviews are focused, disciplined, and actually finished.

The data on peer review’s effectiveness has been around long enough to buy its own whiskey. Steve McConnell published these numbers in Code Complete back in 1993. Nobody has meaningfully contradicted them since, which tells you something about how well the industry has been listening.

The average defect detection rate is only:

• 25% for unit testing
• 35% for functional testing
• 45% for integration testing

In contrast, the average effectiveness of design and code inspections is 55% and 60%—an impressive statistic indeed. Of course, it only pans out if what you’re doing in peer review is effective and efficient.

Over the years, I’ve witnessed the many pitfalls that lead to ineffective peer reviews. Avoiding these bad habits may just be as effective as adopting good new ones! Eliminate the bad habits below to avoid ineffective peer reviews.

8 Bad Habits to Avoid for Successful Peer Code Reviews

Despite the critical role of peer code reviews in software development, they can only be worth it if conducted effectively and efficiently. A badly conducted peer review can lead to wasted time with no meaningful feedback for code improvement. To ensure success in your code review, here are eight habits to avoid.

1. Underutilizing Tools in Peer Code Reviews

To start, you shouldn’t be reviewing or looking for anything that can be done by static analysis. This could include branding issues, style issues like curly placement {
don’t get me started;
},
or using a specific encryption algorithm. If a tool can find it for you, let it.

In the age of LLMs, this applies more than ever. If you’re spending review time on something a model could flag in seconds, you’re wasting the most valuable resource in the room: human judgment.

Free yourself to look deeper into the algorithm, security, and performance characteristics of the code. That’s the work worth doing, and it’s also more interesting to participate in.

2. Reviewing Unfinished Code

This is a classic problem that especially pervades calendar-centric organizations. Chances are if you release based on a date, you also review based on a date. The logic goes like this: "I’m not done yet, but we’ve already scheduled a review so let’s at least look at what we have." You know it as well as I do—it isn’t a great way to do an effective review, so stop doing it. Make sure the code author is finished, and if they’re not ready yet, postpone until they are.

3. Overextending Peer Code Review Sessions

Lengthy code review sessions can be counterproductive. It’s important to set reasonable limits on the scope and duration of each review to maintain focus and productivity. Long, exhaustive reviews can lead to fatigue, reduced attention to detail, and a slower development process.

If the review is taking too much time, you need to rethink something. Too much in this instance could be either review sessions that are over an hour or sessions that consume too much of the overall development schedule. If reviews take more than an hour, you’re probably trying to review too much at once. Or the author wasn’t ready for the review. After an hour of review, potential effectiveness declines fast, especially for the code authors. Even if the commentary wasn’t initially personal, after an unnecessarily long review, the critique can compound and feel more painful.

4. Personalizing Peer Code Review Feedback

A peer review is about the code, not the people. Make sure you’re talking about the code and not the developer. A statement like, "This code won’t scale as well as we need" is less likely to offend than, "You wrote this badly." Conversely, when you’re on the receiving end of critique, be a good sportsman. Recognize that everyone’s code can be improved and you can learn from the data you’re getting. No matter which end of the review you’re on, you can probably be more graceful in facilitating a smooth, pleasant, and speedy review. Think of reviewing as a great way to get a free mentor. Everyone wants to get a mentor, but we rarely think about the review process as a mentoring process. Valuing the mentorship may help you resist taking it personally.

5. Procrastinating Peer Code Reviews

It’s not just a New Year’s resolution metaphor. I really am a big believer that most software quality practices should be treated like exercise. If you try to binge them at the last moment, they won’t be effective. You can’t run a treadmill the night before a marathon, and you can’t do your peer review the night before your release.

Here’s the thing about last-minute reviews: the issues you find then are rarely the ones you can actually fix.

Simple style bugs? Your static analysis tool probably already flagged those.
Naming inconsistencies? LLMs catch those in seconds.

What peer review tends to surface at the eleventh hour are the things you genuinely can’t patch in a sprint—a poor framework choice, a performance architecture that won’t scale, security assumptions baked into the design.

Those aren’t bugs you fix overnight. They’re decisions you unmake at great cost, if at all. Review early enough that you can actually do something with what you find. Finding a fundamental problem the night before release isn’t a successful review—it’s just an expensive way to confirm you waited too long

6. Inconsistent Follow-Up in Peer Code Review Processes

How a review is followed up can greatly affect the value of the review. If you find items during a review and don’t check that they’re fixed, you’re probably wasting your time. The best benefit is found in a consistent process that includes accountability. Make sure everyone knows what is expected of them and follow up to make sure fixes are being made.

Pay attention to patterns in your review results. If a particular codebase, developer, or time in the schedule consistently produces reviews with nothing found, that’s a signal worth investigating. Not an invitation to manufacture issues, but a reason to ask why.

I’ve seen developers review each other’s code in a tidy mutual arrangement: issues logged, issues closed, nothing ever unfinished, everything wrapped up with implausible efficiency. Unbelievably efficient. Literally unbelievable.
Empty reviews aren’t proof of perfect code. They’re often proof of a broken review process.

7. Inconsistent Criteria for Peer Code Reviews

If each reviewer isn’t looking for the same things, you will have no idea if your reviews are effective. The scope of peer review must be based on an unambiguous policy that’s clearly written down and can be referenced. Having a checklist may feel constricting at first, but it will help keep the review on track and serve double duty if you’re in a compliance industry like automotive or medical where you need to prove that you’ve done an effective review.

Eliminating ambiguity takes more discipline than just writing down the policy, although that’s a great first step. Ideally, you’d flesh out a couple of scenarios based on your policy and ask different people how they’d do it. It’s not uncommon to find companies accepting a status quo where different groups do things differently, and both think the other group is doing it wrong, but both are being allowed under the ambiguous policy. You can do better.

Get everyone on the same page. It will improve your quality, provide the consistency necessary for assessment and improvement, and protect you if something goes wrong. If you’re in a compliance environment like ISO 26262 or FDA, having consistent criteria will streamline your audits.

I’ve witnessed peer reviews at a wide variety of organizations and seen where it can be unbelievably worthwhile, as well as a complete waste of time. And that was before LLMs entered the picture.

8. One More Thing: Mishandling LLMs in Peer Code Review

LLMs have entered the code review conversation, whether your organization has a formal policy about them or not. That cuts both ways.

• Used deliberately, they make human reviewers more effective.
• Used carelessly, they create the illusion of rigor without the substance.

Both failure modes are worth knowing.

Using LLMs as a Prereview Sanity Check

One of the more practical uses of LLMs right now is as a first pass before you put code in front of a human reviewer. Think of it as the code review equivalent of reading your own email aloud before sending it.

Asking an LLM to review your diff—looking for logical errors, unclear naming, missing edge cases, or places where the intent doesn’t match the implementation—catches the embarrassing stuff before it wastes a colleague’s time.

This also helps with habit #3 above. If you’re going into a 45-minute review session and the LLM has already flagged three things you fixed, the human session is shorter and more focused. That’s a win for everyone in the room.

That said, be specific about what you’re asking. "Review my code" is a weak prompt.
"Review this function for error handling gaps, and flag anything that looks like it assumes happy-path inputs" is better. LLMs are responsive to scope. They’ll meander if you let them.

What to Watch Out for When LLMs Do the Reviewing

LLMs are confident reviewers, which is part of the problem. They’ll produce authoritative-sounding feedback on code they fundamentally misunderstand—especially when context is limited, the codebase is large, or the logic depends on domain knowledge that never made it into the prompt.

Treat their output the way you’d treat feedback from a smart intern on day one: useful for obvious things, unreliable for anything subtle.

A few specific failure modes to watch for:

• Hallucinated standards. LLMs may cite rules, best practices, or API behaviors that don’t exist or don’t apply to your context. If a review comment references something specific, verify it.
• Style over substance. They’re very good at flagging formatting and naming. They’re less reliable at catching architectural problems or race conditions. Refer back to habit #1: LLM style feedback is a tool output. Don’t let it crowd out the substantive review.
• False closure. "The LLM reviewed it" is not a substitute for human review, and it especially isn’t a substitute for a compliance-mandated review. If your process requires a documented peer review, an LLM interaction doesn’t satisfy that—and if you’re in a regulated environment like ISO 26262 or IEC 62304, the documentation requirements are not flexible on this point.

Used right, LLMs make human reviewers more effective by doing the obvious legwork first. Used carelessly, they create the illusion of rigor without the substance. That’s a bad trade in any development context and a potentially serious one in safety-critical work.

Conclusion: Maximizing the Value of Peer Code Reviews

In conclusion, peer code reviews hold immense potential to enhance the software development process and ensure the delivery of high-quality software.

For developers, engaging in effective peer code reviews offers several key benefits, such as providing a valuable learning opportunity that exposes them to different coding styles, best practices, and diverse problem-solving approaches within their team. This knowledge-sharing fosters professional growth and skill development. Additionally, peer code reviews help developers catch and address defects early, as well as contribute to codebase consistency and maintainability.

To support successful code review processes, leveraging static analysis tools can be invaluable. These tools automatically analyze code for potential issues, such as coding standard violations, security vulnerabilities, and code smells.

For C, C++, Java, C# and VB.NET, Parasoft offers static code analysis tools like C/C++test, Jtest, and dotTEST, all of which have AI and ML capabilities and are designed for varying development environments. By integrating static analysis tools into the code review workflow, teams can enhance the efficiency and effectiveness of their reviews. Combining the human expertise of peer reviewers with the automation provided by static analysis tools optimizes the value and impact of peer code reviews in the software development life cycle.