Sourcery AI: how to review code when an AI has silently refactored your Python before the commit

2026-04-30 · 5 min read · ZenCode

Sourcery AI is a Python code quality tool that operates at two points in a developer’s workflow: in the editor as a real-time refactoring assistant, and as a GitHub App that reviews pull requests and suggests improvements inline. In the editor, Sourcery watches as you type and offers one-click refactors — extracting functions, simplifying conditionals, reducing nesting, removing dead code. In the GitHub App mode, it posts review comments with suggested changes directly on the PR diff before any human reviewer opens the pull request. In both modes, the defining characteristic is that Sourcery’s transformations land before review. Code that reaches the commit or the PR has often already been refactored, simplified, or restructured by an AI that had no knowledge of why the original structure existed.

That silent, pre-review transformation is what makes Sourcery worth examining carefully as a code review problem. CodeRabbit and Ellipsis AI post reviews that arrive before the human, shaping attention. Sourcery goes further: it changes the code before the human ever sees it. The three review traps below are specific to that earlier intervention point.

The three Sourcery AI review traps

1. Invisible provenance

When a developer accepts Sourcery’s inline refactor in the editor — clicking the lightbulb, applying the one-click suggestion — the resulting code looks like code they wrote. There is no marker in the file, no comment, no annotation that says this block was restructured by Sourcery. It goes into version control as the author’s change. When a reviewer reads the PR, they see a diff that represents one person’s apparent decisions. In practice, they are reviewing a collaboration between the developer and Sourcery’s model, with no way to distinguish which parts belong to each.

This provenance problem creates a specific failure mode: the reviewer asks “why did you structure it this way?” and the author doesn’t know, because Sourcery suggested it and they accepted it without fully examining the tradeoff. The reviewer, assuming authorial intent, interprets the structure as a decision and evaluates it accordingly. Both parties are reviewing different things — the reviewer is looking for intent, the author is unsure what they intended.

The fix requires the author, not the reviewer, to act before committing. Every Sourcery suggestion accepted in the editor should be read as a question: do I understand why this is better, and is it correct? If the answer is no, the refactor should not be accepted silently. Sourcery’s suggestions are frequently correct from a style and complexity standpoint, but correctness on those dimensions does not guarantee correctness in logic, edge cases, or business context. Accepting a refactor without understanding it delegates that judgment to the model, and the model has no access to the context that makes the original structure meaningful.

2. Complexity-metric confidence

Sourcery surfaces cyclomatic complexity scores, method length metrics, and quality grades in the editor and in PR comments. A function that starts with a C grade and ends with an A grade after Sourcery’s refactor looks, by those metrics, like improved code. This numerical improvement creates a specific confidence trap: reviewers who see that Sourcery has flagged and reduced complexity assume the substantive review work has been done. They focus their attention on the remaining ungraded areas and give less scrutiny to the code Sourcery already “fixed.”

Complexity metrics measure one narrow dimension of code quality: structural complexity as counted by the number of decision branches. They say nothing about whether the logic is correct, whether the abstraction is the right level for the surrounding system, whether the helper function Sourcery extracted will be reused or will become dead code in six months, or whether the simplified conditional actually handles the same set of input states as the original. A function with a Sourcery A grade can have a significant bug. The grade means the structure is clean. It does not mean the structure is right.

The practical fix is to treat Sourcery’s grade as noise during the review pass. Look at the code, not the score. Amazon CodeGuru Reviewer creates the same trap from a different direction — ML-confidence scores on individual findings — and the same defense applies: the metric is a filter on one dimension, not a judgment on the whole. An A-graded function with a logic error is still a bug. A C-graded function that correctly handles a rare edge case is still correct.

3. Interleaved-change blindness

The most operationally tricky Sourcery trap emerges in PR diffs where both developer logic changes and Sourcery-accepted refactors appear in the same file. A typical diff might show: a new function the developer wrote, a restructured loop that Sourcery simplified, a renamed variable that Sourcery suggested, another new function, a flattened conditional from Sourcery. The reviewer sees one contiguous diff. Nothing in the presentation distinguishes the developer’s semantic changes from Sourcery’s structural ones.

This interleaving is where review attention is most easily misdirected. A reviewer spending time evaluating whether Sourcery’s loop restructure preserved the original semantics — a question that often requires careful analysis — is spending review budget on a style refactor instead of on the logic that actually changed behavior. The reverse is also a problem: a reviewer who learns to dismiss Sourcery-style structural changes as noise stops examining them and misses the cases where the structural change altered semantics in a way that matters.

Neither wholesale trust nor wholesale dismissal of the Sourcery-refactored sections is correct. The right approach is to separate the two categories before reviewing. When a PR contains Sourcery-accepted changes, the author should indicate which diff hunks are Sourcery refactors and which are their own semantic changes — either through a PR description note, a separate commit, or an inline comment. This separation is not about accountability; it is about giving the reviewer a map so their attention goes where semantic correctness is at stake. GitHub Copilot Autofix creates a similar interleaving problem when its security patches land in the same commit as the developer’s feature changes. In both cases, the fix is separation before the review opens, not discipline during it.

How to use Sourcery AI without losing review quality

None of these traps argue against Sourcery. Reducing cyclomatic complexity in Python is genuinely valuable, and Sourcery’s refactors are frequently correct and clean. The traps follow directly from how that value is delivered: silently, before review, integrated into code that reviewers will attribute to a single author with consistent intent.

Three practices keep Sourcery productive without degrading the review signal. First, understand every Sourcery suggestion before accepting it in the editor — not because the suggestion is likely wrong, but because “Sourcery suggested it” is not an explanation a reviewer can work with. Second, ignore Sourcery’s complexity grades during review; they measure structure, not correctness. Third, when submitting a PR that includes accepted Sourcery refactors, call them out in the description: this tells reviewers where to spend their time and prevents interleaved-change blindness from directing attention to style changes at the expense of logic changes.

The review traps above are not unique to Sourcery. They apply to any tool that transforms code before the review surface — Snyk Code when it auto-fixes security issues, Sweep AI when it opens a fixing PR, any AI assistant whose suggestions get accepted without a deliberate review step. Sourcery is distinctive because it operates in the editor at the moment of writing, which puts the intervention earlier in the workflow than any of those. Earlier is not more dangerous, but it is harder to track — which is why the habits above need to be the author’s habit, not just the reviewer’s.

Related reading: CodeRabbit and Ellipsis AI both post bot reviews that land before the human — a later intervention point than Sourcery but the same anchoring risk. Amazon CodeGuru Reviewer uses ML confidence scores to flag issues, creating similar metric-confidence traps. GitHub Copilot Autofix interleaves AI-generated security patches with developer changes in the same commit. Snyk Code auto-fixes security issues that may or may not preserve surrounding semantics. How to review AI-generated code covers the general checklist for evaluating code from any automated source.