How Code Coverage Drives Quality: A Proven Approach

 

How Code Coverage Drives Quality: A Proven Approach

The way we create quality software has evolved dramatically over the years. One of the most significant shifts I’ve observed is the responsibility for quality moving squarely onto the shoulders of those writing the code: the engineers. It’s no longer acceptable to rely on QA teams or others downstream to ensure that what we release to customers meets the highest standards. Quality is everyone’s job, and it starts with the code we write.

One powerful tool to promote this accountability and improve overall quality is code coverage metrics. By requiring engineers to measure and meet coverage goals, we nudge them toward building automated tests that not only prevent bugs today but also safeguard the codebase as it evolves.



The Impact of Requiring 80% Code Coverage

Years ago, my teams implemented a requirement: every new code check-in must have at least 80% code coverage. It took a little bit of time but the results were incredible. Within just 30 days, we saw an 80% reduction in customer escalations. This wasn’t a fluke—our customers were noticing and appreciating the improvement in quality, and the development team began to feel the impact as well.

Initially, there was resistance. Engineers feared that enforcing a coverage requirement would slow down innovation and create unnecessary bottlenecks. But within 60–90 days, the same skeptics became converts. One engineer, who initially “hated” the idea, later said, “This saved me from releasing so many bugs. I don’t want to code without this again.”

What changed their minds? The process forced them to consider edge cases and explore all possible code paths as they tried to get enough coverage to meet the requirement. This thoroughness not only translated into fewer defects and better reliability, but also surfaced bugs that might have otherwise gone unnoticed and made their way to customers. By requiring tests to achieve the coverage threshold, engineers often discovered gaps in their logic or unexpected interactions within the code. The process wasn’t just about prevention; it was about uncovering and fixing issues before they could cause harm. Once they experienced these benefits, there was no going back.

Getting Started: A Simple, Scalable Approach

One of the best things about this approach is its simplicity. You don’t have to overhaul your entire codebase overnight. Instead, you can start small:

  1. Apply the 80% code coverage requirement only to new code check-ins.
  2. Leave existing code as-is, unless you modify it. If changes are made, those updates must meet the same coverage requirements.
  3. Gradually, as the team continues to deliver features and fixes, automated tests will naturally grow, creating a more robust codebase.

This incremental approach avoids overwhelming teams and allows them to focus on what matters most: delivering value while improving quality over time.

Why It Works: Embedding Quality in the Process

High-quality software isn’t an accident; it’s the result of intentional processes, cultural shifts, and metrics that guide decision-making. Here’s how these elements come together:

  1. Processes: Agile methodologies with integrated quality gates ensure that issues are caught early. Requiring automated tests and code reviews enforces a higher standard.
  2. Culture: When engineers are accountable for the quality of their code, they take pride in building stable, maintainable solutions.
  3. Metrics: By tracking code coverage, defect rates, and customer-reported issues, teams gain insights into areas of improvement and can measure success effectively.

The Broader Impact: Quality as a Competitive Advantage

When you focus on quality, the results extend far beyond your team. Customer satisfaction improves, trust in your product grows, and your engineers can innovate with confidence. As technical debt decreases, you’ll see faster delivery cycles and fewer headaches down the road.

Anyone can implement this strategy—it doesn’t matter where your codebase is today. Start small, focus on new code, and watch the transformation unfold. I’ve seen it work firsthand, and I believe it can make a meaningful impact on your organization too.

What are your thoughts? Have you used code coverage or similar strategies to improve quality in your team? I’d love to hear your experiences in the comments below.

Comments

Post a Comment

Popular posts from this blog

AI Coding Tools, One Year Later: What’s Really Changed?

Image
As an engineering leader, I am always looking for ways to help my engineering team not only achieve the goals of the organization but really enjoy what they are doing. They love to code, and I want to help them do that. Part of that means taking time to find ways and tools to get the right outcomes. So how can AI help with that? A year or so ago, I set out to see how great the AI tools really were at replacing software engineering efforts. I learned a lot, and they absolutely made me a better engineer, but it still felt like they had a long way to go. Recently, I’ve been hearing from some big hitters in the industry that AI coders are now much closer to replacing engineers. Some claims even say that premium services offer AI coding bots that could actually replace a lot of engineering work. Even though I didn’t have access to those premium services, I decided it was time to jump back in and retest the waters with the newly improved tools. How much better could they really be? My Proje...
Read more

Using AI to Improve Code Quality: A Practical Approach

Image
Everyone is searching for the most efficient and effective way to use AI in software development. So how do we take advantage of AI and where is the best place to start?   AI excels at generating code, but it's not always perfect for what you are looking for.  Ensuring consistency, adhering to code patterns, and maintaining structure requires engineers with experience. Based on my experience, approximately 90% of AI-generated code still needs some modifications or tweaking to align with best practices, scalability, and ongoing maintainability. This is where experienced engineers play a critical role in shaping AI-assisted code into production-ready solutions. AI as a Strategic Tool in Engineering Teams Over the past several years, I have worked closely with engineering teams to determine how AI can be used reliably and consistently in the development process. While AI can be leveraged across multiple stages of coding, one of the best starting points is improving code cove...
Read more

Exploring AI Coding Tools: Can They Really Build Software for You?

Image
TL;DR   AI tools like ChatGPT-4 and GitHub Copilot, in their current state, have incredible potential to boost productivity. While they still require substantial hand-holding, reviewing, and slight alterations at nearly every step, they enabled me to complete my project 75%-80% faster thanks to my coding expertise. By using these tools, I was significantly more capable of getting features done in far less time. Though AI coding companions are promising, they are not yet fully autonomous—engineers remain essential in guiding and refining the work to achieve optimal results. It will be interesting to see how long it takes before these tools evolve to a point where engineers may not be needed for the majority of the development process.  For the full story, continue reading below. Introduction: My Objective  As an engineering leader with 20 years of development experience, I aim to help my engineering teams work faster and more efficiently. To do that, I wanted to explore th...
Read more