Senior Devs Using Claude Code vs Manual Coding: A 30-Day Experiment

In September 2025, we ran a controlled experiment at Fordel Studios. Six senior developers, each with 8 or more years of experience, used Claude Code as their primary coding assistant for 30 days. Six matched developers continued with their existing workflows as a control group. We tracked time-to-completion, code quality metrics, test coverage, and self-reported satisfaction.

The headline result: Claude Code users completed tasks 40% faster on average. But that number hides a more interesting story about where AI assistance actually helps and where it actively gets in the way.

Let us start with the setup. All 12 developers worked on the same codebase, a mid-size Next.js application with a Go backend and PostgreSQL database. Tasks were randomly assigned from a shared backlog and categorized into four types: new features, bug fixes, refactoring, and infrastructure work. Each developer logged their time using Toggl with task-type tags.

The 40% speed improvement was not evenly distributed across task types. For new feature development, Claude Code users were 55% faster. For bug fixes, they were only 15% faster. For refactoring, the improvement was 35%. And for infrastructure work like CI/CD changes, deployment configs, and database migrations, Claude Code users were actually 10% slower than the control group.

That last data point needs explanation. Infrastructure work involves highly context-dependent configuration where a small mistake can take down production. Several Claude Code users reported that the AI would confidently suggest Terraform configurations or Docker Compose changes that looked correct but had subtle issues. One developer spent 3 hours debugging a Kubernetes manifest that Claude had generated with an incorrect resource limit format. The manual coding group, working more carefully from documentation, avoided these pitfalls.

New feature development is where Claude Code truly shines for senior developers. The key word there is "senior." When an experienced developer knows exactly what they want to build and can evaluate the AI's output critically, the workflow becomes remarkably efficient. One developer described it as "having a very fast junior developer who never gets tired and types 200 words per minute." You tell it what you need, it generates a first draft, you reshape it into production-quality code.

The senior developers developed distinct patterns for working with Claude Code over the 30 days. The most effective pattern, used by 4 of the 6 developers by day 15, was what one called "scaffold and refine." They would describe the feature at a high level, let Claude generate the initial structure, then manually rewrite the critical logic while keeping the boilerplate. This approach worked because boilerplate code like API route handlers, form components, and database query builders is where most development time goes, but it is also the code least likely to contain subtle bugs.

The least effective pattern was "generate and hope," where the developer would ask Claude to implement an entire feature and then try to review the output as a whole. This approach led to more bugs and often took longer than manual coding because reviewing 300 lines of generated code for correctness is harder than writing 300 lines yourself when the logic is complex.

Code quality metrics told an interesting story. We measured cyclomatic complexity, test coverage, and the number of bugs found in code review. Claude Code users had slightly higher cyclomatic complexity on average, primarily because the AI tends to generate more explicit conditional handling rather than using abstractions. Test coverage was nearly identical between the two groups, around 78% for both, which suggests that Claude Code does not meaningfully help or hurt testing practices for senior developers.

Bug rates in code review were where things got nuanced. Claude Code users had 20% fewer syntactic and structural bugs, things like missing error handling, incorrect type annotations, or broken imports. But they had 30% more logical bugs, cases where the code ran correctly but did not implement the business logic as specified. This makes intuitive sense. Claude Code is excellent at writing structurally sound code but cannot know your business rules unless you explicitly describe every edge case.

The self-reported satisfaction data was overwhelmingly positive. Five of six Claude Code users said they wanted to continue using it after the experiment. The one holdout, a developer with 15 years of experience specializing in systems programming, said he found it "faster for boring code but distracting for interesting problems." He preferred to think through complex algorithms without AI suggestions interrupting his thought process.

All six developers reported that their relationship with Claude Code changed significantly over the 30 days. In the first week, they over-relied on it, generating too much code and spending too much time reviewing AI output. By week three, they had developed intuition for when to use it and when to code manually. The consensus was that Claude Code is most valuable for code you know how to write but do not want to type, and least valuable for code you are still figuring out how to write.

One unexpected finding: Claude Code users wrote significantly more tests than the control group, not in terms of coverage percentage, but in terms of total test cases. When writing tests is fast and painless because you can describe the test scenario and have the AI generate the test code, developers write more of them. Four of six Claude Code users reported that test-writing went from being a chore to being "almost enjoyable."

Our recommendations after this experiment are specific. Use Claude Code for boilerplate-heavy feature development, CRUD operations, test writing, and documentation. Do not use it for infrastructure-as-code changes, complex algorithmic work, or security-sensitive code without extremely careful review. Invest time in learning effective prompting patterns because the difference between a developer who prompts well and one who prompts poorly is larger than the difference between using Claude Code and not using it at all.

For teams considering adoption, start with a 2-week trial with your most experienced developers. They will develop the patterns and guidelines that make AI-assisted coding effective, which you can then use to onboard the rest of the team. Do not start with junior developers because they lack the judgment to evaluate AI output critically, and they will learn bad habits.

The bottom line: Claude Code makes senior developers about 40% faster overall, with the gains concentrated in feature development and testing. It does not replace engineering judgment, and it introduces new categories of bugs that teams need to learn to catch. Used well, it is the most significant productivity tool we have adopted since switching from SVN to Git.