7 Free Tools for Cleaning Up Inconsistent Naming Before a Refactor

Before you touch a single line of a big rename or convention migration, it helps to actually see the mess you're dealing with. These seven free tools cover the inspection, conversion, and verification steps that come before (and during) a naming cleanup.

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1. A case converter, for the mechanical renaming itself

Once you've decided on a target convention, converting a long list of field names, variables, or column names by hand introduces typos that are hard to spot on review. A dedicated case converter handles camelCase, snake_case, PascalCase, kebab-case, and CONSTANT_CASE conversions in bulk, with a line-by-line mode for converting whole lists at once.

2. A JSON formatter, for seeing your actual API shape clearly

Minified or inconsistently formatted JSON hides naming inconsistencies. A JSON formatter with a tree view makes it much easier to spot the field that's still snake_case in an otherwise camelCase response, because you can actually see the structure instead of scanning one long line.

3. A regex tester, for finding every occurrence before you rename

regex101 lets you build and test a pattern against real sample text before running a find-and-replace across a codebase. Testing the pattern against edge cases (a field name that's a substring of another, for instance) before running it live avoids the classic "renamed id and accidentally renamed valid too" mistake.

4. A text diff tool, for confirming the rename didn't miss anything

Comparing a before-and-after export of your schema or API response, line by line, catches partial renames that a visual scan misses. A dedicated diff tool, whether a browser-based one like a text diff tool or Diffchecker, highlights every changed line instead of making you eyeball two long files side by side.

5. ESLint, for enforcing the convention going forward

ESLint with the camelcase rule (or an equivalent for your language) turns a style guideline into an automatic build failure, which is the only reliable way to keep a naming convention consistent once more than one person is contributing.

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6. A linter for your database migrations

If your naming cleanup touches the database, a SQL linter or formatter that flags inconsistent column naming conventions catches issues before a migration ships, rather than after a query starts failing in production. SQLFluff is a widely used open-source SQL linter that includes naming-convention rules among its checks, and even a basic formatter surfaces columns that don't match the rest of the schema simply by making the whitespace and casing differences visually obvious.

7. Your version control's blame and log tools

Before renaming anything long-established, git log --follow and git blame on the affected files tell you whether the inconsistent name was a recent addition (safe to fix quickly) or has been stable for years with external dependents (worth a more cautious, staged migration). Git's own documentation covers both commands if you're not already using them regularly.

A rough order to actually use these in

Reaching for all seven at once during a cleanup is overkill for a small project and overwhelming for a first pass at a large one. A workable sequence:

1. Audit first. Use git log --follow and git blame to understand how the inconsistency arrived and whether it's recent or long-established. This tells you how cautious the rest of the cleanup needs to be.
2. See the actual shape of the problem. Run your API responses through a JSON formatter and your schema through a SQL linter to get a clear, structural view of exactly which fields are inconsistent, rather than working from a vague sense that "some things feel off."
3. Build and test the rename pattern. Use a regex tester against real sample data before running anything live, catching false-positive matches before they touch a file.
4. Convert in bulk. Once the pattern and target convention are confirmed, a case converter handles the mechanical relabeling faster and more reliably than manual retyping.
5. Verify with a diff. Compare before and after to confirm the rename touched everything it should have and nothing it shouldn't.
6. Lock it in. Add ESLint (or your language's equivalent) so the convention holds going forward without requiring a human to catch every future violation in review.

What to do if the cleanup reveals a deeper problem

Sometimes an audit like this surfaces something bigger than an inconsistent name here and there. If a third or more of your field names don't match your intended convention, that's usually a sign the convention was never actually agreed on as a team, rather than a sign that individual contributors were careless. In that case, the highest-leverage fix isn't running these seven tools harder. It's writing down the convention explicitly, in a place every contributor will actually see it (a CONTRIBUTING file, a linter config that fails the build, a pinned message in your team's chat), before doing the cleanup. Otherwise the same drift creeps back in six months later from a new contributor who never saw the informal agreement.

How long this actually takes

For a project in the low tens of thousands of lines, a full naming audit and cleanup using this toolchain typically takes a few focused hours: an hour for the audit and pattern-testing steps, an hour or two for the actual conversion and verification, and ongoing near-zero effort afterward once ESLint or an equivalent is enforcing the convention automatically. Larger codebases scale roughly linearly with the number of distinct naming patterns involved, not with raw line count, since most of the time is spent understanding the inconsistency, not applying the fix once it's understood.

Teams that skip the audit step and jump straight to renaming based on a quick visual scan often underestimate the scope, discover a second or third inconsistent pattern mid-cleanup, and end up doing the work twice. Spending the first hour purely on inspection, before touching a single file, is what keeps the rest of the process from turning into an open-ended, expanding scope.

If you only take one thing from this list, make it the ordering: inspect before you convert, test before you apply, verify before you consider it done. The tools matter less than doing the steps in that order. Swap in whatever equivalents your stack already uses, a different linter, a different diff viewer, and the sequence still holds.

Putting it together

None of these tools replace the actual decision of which convention to standardize on. That's a team conversation, not a tooling problem, and it's worth having explicitly rather than letting whoever touches a file last decide by default. Once the decision is made, though, this list covers inspection (JSON formatter, regex tester, SQL linter), the mechanical conversion (case converter), verification (diff tool, git blame), and enforcement (ESLint) end to end.

For the reasoning behind picking a convention in the first place, camelCase versus snake_case versus the rest, see https://evvytools.com's longer guide on choosing a naming convention, which covers the ecosystem-matching rule and the translation-boundary pattern that most of these tools support.