Code coverage is a flawed but overused proxy for software quality
If you’re leading a product or engineering team, you’ve probably heard teams throw around “code coverage” metrics like a badge of honor. But here’s the thing, high code coverage doesn’t mean high code quality. It’s not that the metric is useless; it’s that it’s often treated like a target instead of a tool. When that happens, smart teams start optimizing for the number instead of the outcome. That’s when things go sideways.
Code coverage measures what percentage of your source code gets executed when automated tests run. Sounds useful, until you realize it doesn’t account for whether those tests are actually validating anything important. It treats a critical payment validation function and a color theme switcher the same way. Teams write extensive tests for low-value UI features while mission-critical logic goes untested simply because they’re trying to hit an arbitrary 80% coverage threshold.
Jared Toporek, a consultant and developer, broke this down after experiences where teams had to sacrifice meaningful improvements just to keep coverage numbers healthy. His investigation found no scientific evidence that higher code coverage correlates with better quality software. He ran structured experiments, and the data was clear, cleaner code and better coverage metrics aren’t the same thing.
That’s a problem. And it’s not a technical one. It’s a leadership one. If you’re pushing your teams to hit these numbers without questioning what they actually mean, you’re likely slowing down innovation and increasing costs. Focus on substance, how confident are you that your core features won’t break? That’s what matters.
Code coverage metrics fail to account for feature value or business context
All code is not created equal. And yet, most code coverage tools assume it is. That’s inefficient and, frankly, expensive.
The way coverage tools work today, they’ll prompt your team to test every file or function to the same degree. That’s a mistake. You don’t need to test the same way for a file encrypting user payment data as you do for one that adjusts a user’s avatar. Treating both equally wastes your developers’ time, and the ROI just isn’t there. Your critical systems, the ones dealing with money, security, or compliance, absolutely need robust, tested logic. Other parts of the codebase? Not as much.
This is about smart prioritization. Once a business reaches scale, operations are not just about feature deployment. They’re about resource allocation. If your team spends a week writing a test suite that inflates coverage numbers but doesn’t protect any real business risk, you’re not being efficient. You’re creating drag.
Jared Toporek points this out with refreshing clarity. In his experience, most teams using default code coverage tools never customize the threshold. They just apply the same rule uniformly, usually 80%—across all parts of the application. This includes outdated legacy code, new experimental features, and everything in between. The cost? Time. And it adds up fast.
If you’re an executive, this is worth your attention. Because if your teams aren’t distinguishing between high-impact and low-impact features when testing, your product’s resilience is only as strong as its least valuable module. That’s not the risk profile you want in production. You need a sharper lens and a testing strategy that works backward from value, not forward from metrics.
The 80% coverage threshold is an arbitrary and misunderstood application of the pareto principle
The 80% code coverage threshold is everywhere. It’s in your CI/CD pipelines, in your dashboards, in the reporting metrics you’re reviewing at the end of every sprint. But let’s get to the core: that number has no evidence-backed origin. It didn’t come from a rigorous study. It didn’t emerge from decades of software engineering research. It was guessed. And worse, it was misunderstood.
Some believe it came from a bad interpretation of the Pareto Principle, the idea that 80% of outcomes stem from 20% of causes. Even if applied correctly, the implication would be to find the most critical 20% of your application and focus your testing efforts there. But the way 80% is used in code coverage does the opposite. It distributes testing effort evenly across all code, ignoring feature value and impact.
Executives need to recognize the consequence of leading teams with poorly defined quantitative rules. If you’re enforcing 80% coverage without evaluating why it exists and what it achieves, you’re creating a policy rooted in misconception. Leadership isn’t about copying defaults, it’s about applying judgment and context.
Jared Toporek points this out clearly. He couldn’t find a single validated study that shows 80% is the right number. No cost-benefit model, no systemic testing comparison. Just a default setting repeated across tools because it sounds serious and familiar. Many teams follow it out of habit, not effectiveness. That’s a gap in strategic thinking that should be closed.
Efforts to improve code quality by making code DRYer can unintentionally reduce measured code coverage
Well-written, maintainable code often consolidates logic, removing duplication and centralizing behavior. This follows the DRY (Don’t Repeat Yourself) principle. It’s efficient. It reduces bugs. It simplifies long-term maintenance. But under current tools, it can also lower code coverage ratings. So teams often face a false choice: improve engineering standards or preserve coverage metrics. That’s not a tradeoff you want to enforce.
Here’s how this happens: when repeated logic is refactored into a shared function, your total lines of code go down. Code coverage tools then recalculate proportions. Even if the important behavior is still being tested, the percentage may dip. Suddenly, your pipeline blocks the merge because coverage dropped below 80%. Not because quality went down, but because math changed.
This metric pressure means developers may avoid improving the architecture during bug fixes or feature work, because doing so could trigger enforcement issues. That’s technical debt being baked in. And that slows innovation and increases hidden risk.
Jared Toporek describes a real example. He was fixing a bug and saw an opportunity to make the codebase cleaner and DRYer. But doing that made test coverage drop just enough to block the commit. The result? A better codebase was made worse in service of a meaningless threshold.
Leadership needs to understand: not all drops in coverage signal lower quality. Sometimes they mean things just got better. The right move is to empower teams to improve architecture without fearing arbitrary regression in metrics. Encourage smarter code, not smarter math on reports.
Code coverage metrics can be manipulated by implementing superficial fixes
Code coverage can be gamed. When hitting a number becomes the objective, people find ways to do it, regardless of whether it improves the end product. Developers know the system. Padding existing test paths, injecting log statements, or shrinking lines of code in uncovered sections, all of this affects the metric without moving the needle on actual reliability.
Once you realize this, it becomes clear that code coverage isn’t a solid foundation for quality assurance unless it’s backed by discipline and intent. A test hitting a function isn’t the same as a test validating its business logic under real conditions. When metrics dominate and judgment fades, teams default to shortcuts that meet policies but fail to protect against failure.
Jared Toporek makes this dynamic evident. After refactoring code to follow best practices, he encountered a coverage dip that blocked the commit. Rather than writing meaningful tests for hard-to-reach conditions, the pressure could have driven a less-disciplined developer to inject meaningless lines just to recover the score. That kind of behavior goes unchecked in many environments because tools can’t assess test intent, they assess numbers.
If you’re running product or engineering, you need to be aware of this tendency. It’s not always visible in reports. But the effects compound over time. Your teams might be “passing” code coverage checks with technically compliant, but functionally useless, tests. They passed the test but missed the truth. Don’t reward that.
Automated testing is not always the most cost-effective or practical solution for feature validation
There’s a strong push toward automating everything, and in many cases, that’s right. But automation has a cost. Writing and maintaining automated tests takes time and talent. For some features, especially those that change infrequently or have minimal risk, that cost may never be recouped.
Just because something can be automated doesn’t mean it should be. In feature sets that are rarely used, or where change velocity is low, manual testing might offer better ROI. Jared Toporek lays this out with practical math: if it takes 960 minutes to write an automated test, and 5 minutes to run a manual check, you won’t see a productivity win until 192 deployments of that feature. That’s not speculative, it’s math, and it’s conservative.
Now add in the hidden costs, developer time, CI/CD pipeline delays, cloud compute for test execution. If the test fails due to a code change in some unrelated dependency, you pay the opportunity cost of investigating false positives. Over time, these costs accumulate and slow teams down.
C-suite leaders need to recognize where automation matters most: in systems with high transaction volume, integration complexity, or reputational risk. For legacy features or one-off workflows, the balance may shift. Testing is about confidence. The method you use should depend on the level of confidence required and the cost of failure, not on a blanket policy to automate everything.
Clever engineering is knowing when to say yes to automation, and when it’s just bloat.
Concise coding practices can inflate code coverage metrics
Clean, minimal code is generally a good thing. But when it comes to coverage metrics, less code can lead to misleading data. Concise functions often mask the parts that aren’t adequately tested. That’s a risk, especially if leadership is relying on those metrics to gauge product reliability.
Jared Toporek ran controlled tests that show this problem clearly. He took identical logic and implemented it in six different ways, from highly abstracted to more manually structured versions. The concise version, using simple conditionals, reported 100% code coverage even when 75% of the tests were disabled. Meanwhile, verbose versions exposed missing test paths immediately. The message is simple: when code collapses multiple decisions into a single line, coverage tools can’t diagnose gaps as effectively.
This is a tooling limitation more than a developer mistake. But if leaders aren’t aware of it, they may make decisions based on inflated confidence. A smart CTO or engineering lead should regularly challenge reported coverage metrics to ensure they reflect meaningful test scenarios, not just syntactic hits.
Testing strategy isn’t just about whether code is running during tests, it’s about whether the logic paths are being validated. Concise code has benefits, but it makes metric interpretation harder. Adjust for that by complementing code coverage with smarter test planning, especially for logic-heavy functions.
Code coverage should be applied strategically to balance cost with risk management
Requirements around code coverage often assume a budget of infinite time and resources. That’s rarely the case. Just like any strategic investment, how much testing you do should reflect what you stand to lose if things go wrong. High-risk components need more attention. Low-risk paths don’t need the same investment. That isn’t cutting corners, it’s managing intelligently.
Jared Toporek frames this in terms of return on effort. Every test you write and maintain adds cost, developer hours, pipeline execution, debugging failed tests. Applying 80% or higher coverage across the board ignores the varying value of what’s being tested. A function that processes financial transactions warrants far more thorough testing than a UI element that allows user profile updates.
For executives and technology strategists, this means focusing on strategic test placement. Identify business-critical flows, compliance-sensitive features, and core infrastructure. Make sure those areas are bulletproof. But don’t mandate identical testing standards for everything else. That dilutes your testing power.
Quality assurance is about meaningful protection, not metrics for the sake of metrics. Smart resource allocation, especially in QA, requires you to look past thresholds and understand risk in context. The ROI on testing isn’t evenly distributed. Treat it that way.
Recap
Code coverage tools aren’t the problem. The problem is how they’re used. Relying on a single percentage to represent software quality is lazy thinking, it reduces complex engineering decisions to a superficial metric. Teams optimize for the number. The actual risks? They stay hidden.
If you’re leading product, engineering, or digital transformation, your job isn’t to enforce arbitrary rules. Your job is to create systems that prioritize reliability and smart delivery. That means testing strategically, identifying critical flows, and trusting your teams to focus on real value, not on gaming dashboards.
No one should be writing code fast just to pass a gate. And no one should be blocked from shipping smarter changes because a blanket rule said 79% wasn’t good enough. Treat code coverage like any business metric, with context, purpose, and healthy skepticism.
Make room for engineering judgment. Reward risk-based thinking. And above all, don’t confuse activity with progress. Clean code, well-placed tests, and intentional trade-offs drive strong software. The metrics should support that, not replace it.
