5 Ways to Improve Code Quality Without Slowing Your Team Down

Improving code quality does not have to come at the expense of speed. In fact, better quality often enables faster progress by reducing bugs, rework, and friction in development. High-performing software teams show that you can go fast by going well: delivering changes rapidly while maintaining robust, clean code.

The following five strategies help engineering leaders raise code quality and reduce risk without creating process drag. They focus on making each change easier to review, test, and ship, so velocity and quality improve together.

1. Smaller Pull Requests

Break down development work into smaller, focused pull requests (PRs) instead of large, monolithic changes.

Large PRs are hard to review and often lead to delayed feedback or superficial approvals. Smaller pull requests are quicker to review and less error-prone. Research at Microsoft found that code review effectiveness drops as change size grows, and it explicitly recommends submitting smaller, incremental changes whenever possible.

With small PRs, reviewers can focus and provide quality feedback without being overwhelmed. Developers also integrate changes more frequently, avoiding long-lived [Work in Progress (WIP)] branches that risk merge conflicts and stale code. Smaller PRs encourage frequent testing and deployment, which reduces the scope of bugs and speeds up the feedback loop.

Helpful Metrics:

• [Pull Request Size]: Monitor average lines of code or files changed per PR to keep changes manageable.
• [Pull Request Frequency]: Track how often developers open or merge PRs. Higher frequency with smaller size indicates smooth, continuous flow.
• [Work in Progress (WIP)]: Measure how many PRs or feature branches are open at a time. Lower WIP shows the team is completing work quickly, reducing context-switching overhead.

2. Automate Low-Level Checks in CI

Offload trivial and repetitive code checks to Continuous Integration (CI) pipelines and automated tools, so reviewers can focus on substantive issues.

Save engineers’ review energy for catching logic mistakes, design flaws, and maintainability issues. Automated linters, formatters, static analysis, and unit tests flag and fix issues before a human looks at the code.

Automated analysis reliably catches formatting and other low-level issues that would otherwise waste reviewer time. Google, for example, integrates static analysis with code review to highlight common issues automatically. By automating checks in CI, teams ensure that every PR meets baseline quality standards without extra effort. This leads to faster reviews and fewer back-and-forth cycles.

Helpful Metrics:

• [Pipeline Success Rate]: Track the percentage of CI runs that pass on the first try. High rates indicate issues are caught early.
• [First-Time Pass Rate]: Measure how often code passes all checks and gets approved without resubmission.
• Review Turnaround Time: Observe the time from PR creation to first review completion. Efficient CI checks reduce this latency.

3. Invest in Review Quality

Make code review quality a priority by dedicating time for thorough reviews, adopting best practices, and fostering a healthy review culture

High-quality reviews are one of the most effective tools for improving code before it ships. Engineers should treat reviews as a first-class responsibility. They need time to review code thoughtfully rather than rubber-stamping. Research suggests reviewers should set aside focused time, review small chunks, and provide feedback quickly.

Using review checklists improves consistency. Pairing less-experienced developers with veteran reviewers raises feedback quality and spreads knowledge. Leadership should recognize thorough reviewers and make review part of everyone’s job. A strong review culture prevents costly issues downstream and can actually increase velocity by reducing bugs and rework.

Helpful Metrics:

• Thorough Review Rate (TRR): Tracks the proportion of code receiving substantive review. Higher TRR indicates stronger engagement and more defects caught early.
• PR Lead Time: Measure how long it takes for PRs to be reviewed and approved.
• High-Comment PRs: Track pull requests that required many review comments or revisions. Patterns here may reveal problematic areas of the codebase or mentoring opportunities.

4. Refactor in Small Increments

Continuously refactor code in small, safe increments instead of deferring fixes until massive “overhaul” projects.

Encourage engineers to fold small refactoring steps into daily work. Effective refactoring is done via many tiny, behavior-preserving changes. Each step is low risk and validated with tests. Large, one-time refactoring projects often introduce bugs and block feature work. Incremental refactoring keeps the system stable while steadily improving code health. Over time, the codebase becomes more modular, easier to test, and faster to extend. This reduces technical debt and increases delivery speed.

Helpful Metrics:

• [Change Failure Rate]: Tracks the percentage of deployments resulting in failures. A low or declining rate during refactoring shows improvements are safe.
• [Deployment Frequency]: Measures how often teams ship code. Incremental refactoring should allow frequent deployments.
• [Rework Rate]: The proportion of time spent fixing or redoing work. A decline after adopting incremental refactoring indicates stronger code quality.

5. Measure Quality Outcomes

Use metrics to measure whether quality initiatives are working, and refine processes based on data.

To confirm that quality improvements are happening without hurting delivery, monitor key indicators continuously. Track defect rates and bug frequency to see if code quality improves, and track delivery speed (such as cycle time and deployment frequency) to ensure velocity remains strong. If negative trends emerge, investigate and adjust. DevOps research emphasizes this continuous feedback loop, advocating that teams measure the impact of practices on both output and quality.

Helpful Metrics:

• [Defect Rate]: Track bugs or incidents in production over time. A drop signals effective quality practices.
• [Rework Rate]: Measures the percentage of time spent fixing issues. Lower rates reflect stronger quality and more efficient engineering.
• [Lead Time for Changes] and [Cycle Time]: Track delivery duration and execution speed. Improvements here ensure quality efforts are not slowing delivery.

The Bottom Line

By implementing these five practices, engineering leaders can improve code quality in ways that accelerate rather than hinder output. Smaller PRs, automated checks, and high-quality reviews catch issues early. Incremental refactoring prevents technical debt from piling up. Metrics provide the insight needed to fine-tune processes and prove that quality and speed can advance together.

The result is a development process that delivers robust, clean code with minimal friction. Developers spend less time firefighting, releases ship on schedule, and products grow more reliable with each iteration. Investing in code quality is not a tax on speed but a proven strategy for sustainable, high-performance engineering.