May 16, 2025·8 min
How AI Tools Read Codebases and Refactor Them Safely
Learn how modern AI tools analyze repositories, build context, suggest changes, and reduce risk with tests, reviews, and safe rollout practices.
Learn how modern AI tools analyze repositories, build context, suggest changes, and reduce risk with tests, reviews, and safe rollout practices.
When people say an AI “understands” a codebase, they usually don’t mean human-style comprehension. Most tools aren’t forming a deep mental model of your product, your users, or the history behind every design decision. Instead, they recognize patterns and infer likely intent from what’s explicit: names, structure, conventions, tests, and nearby documentation.
For AI tools, “understanding” is closer to being able to answer practical questions reliably:
This matters because safe changes depend less on cleverness and more on respecting constraints. If a tool can detect the repository’s rules, it’s less likely to introduce subtle mismatches—like using the wrong date format, breaking an API contract, or skipping an authorization check.
Even a strong model will struggle if it’s missing key context: the right modules, the relevant configuration, the tests that encode expected behavior, or the edge cases described in a ticket. Good AI-assisted work starts with assembling the correct slice of the codebase so suggestions are grounded in how your system actually behaves.
AI assistance shines most in well-structured repositories with clear boundaries and good automated tests. The goal isn’t “let the model change anything,” but to extend and refactor in small, reviewable steps—keeping regressions rare, obvious, and easy to roll back.
AI code tools don’t ingest your whole repo with perfect fidelity. They form a working picture from whatever signals you provide (or whatever the tool can retrieve and index). Output quality is tightly tied to input quality and freshness.
Most tools start with the repository itself: application source code, configuration, and the glue that makes it run.
That typically includes build scripts (package manifests, Makefiles, Gradle/Maven files), environment configuration, and infrastructure-as-code. Database migrations are especially important because they encode historical decisions and constraints that aren’t obvious from runtime models alone (for example, a column that must remain nullable for older clients).
What they miss: generated code, vendored dependencies, and huge binary artifacts are often ignored for performance and cost reasons. If critical behavior lives in a generated file or build step, the tool may not “see” it unless you explicitly point it there.
READMEs, API docs, design docs, and ADRs (Architecture Decision Records) provide the “why” behind the “what.” They can clarify things code alone can’t: compatibility promises, non-functional requirements, expected failure modes, and what not to change.
What they miss: documentation is frequently outdated. An AI tool often can’t tell whether an ADR is still valid unless the repository clearly reflects it. If your docs say “we use Redis for caching” but the code removed Redis months ago, the tool may plan changes around a nonexistent component.
Issue threads, PR discussions, and commit history can be valuable for understanding intent—why a function is awkward, why a dependency was pinned, why a seemingly “clean” refactor was reverted.
What they miss: many AI workflows don’t automatically ingest external trackers (Jira, Linear, GitHub Issues) or private PR comments. Even when they do, informal discussions can be ambiguous: a comment like “temporary hack” might actually be a long-term compatibility shim.
Logs, traces, and error reports reveal how the system behaves in production: which endpoints are hot, where timeouts happen, and what errors users actually see. These signals help prioritize safe changes and avoid refactors that destabilize high-traffic paths.
What they miss: runtime data is rarely wired into coding assistants by default, and it can be noisy or incomplete. Without context like deployment versions and sampling rates, a tool may draw the wrong conclusions.
When key inputs are missing—fresh docs, migrations, build steps, runtime constraints—the tool fills gaps with guesses. That increases the chance of subtle breakage: changing a public API signature, violating an invariant enforced only in CI, or removing “unused” code that’s invoked via configuration.
The safest results happen when you treat inputs as part of the change itself: keep docs current, surface constraints in the repo, and make the system’s expectations easy to retrieve.
AI assistants build context in layers: they break code into usable units, create indexes to find those units later, then retrieve a small subset to fit within the model’s limited working memory.
The first step is usually parsing code into chunks that can stand on their own: entire files, or more commonly symbols like functions, classes, interfaces, and methods. Chunking matters because the tool needs to quote and reason over complete definitions (including signatures, docstrings, and nearby helpers), not arbitrary slices of text.
Good chunking also preserves relationships—like “this method belongs to this class” or “this function is exported from this module”—so later retrieval includes the right framing.
After chunking, tools build an index for fast lookup. This often includes:
jwt, bearer, or session)This is why asking for “rate limiting” can surface code that never uses that exact phrase.
At query time, the tool retrieves only the most relevant chunks and places them into the prompt context. Strong retrieval is selective: it pulls the call sites you’re modifying, the definitions they depend on, and the nearby conventions (error handling, logging, types).
For big codebases, tools prioritize “focus areas” (the files you’re touching, the dependency neighborhood, recent changes) and may page through results iteratively: retrieve → draft → notice missing info → retrieve again.
When retrieval grabs the wrong chunks—similarly named functions, outdated modules, test helpers—models can make confident but incorrect edits. A practical defense is to require citations (which file/function each claim comes from) and to review diffs with the retrieved snippets in view.
Once an AI tool has usable context, the next challenge is structural reasoning: understanding how parts of the system connect and how behavior emerges from those connections. This is where tools move beyond reading files in isolation and start modeling the codebase as a graph.
Most codebases are built from modules, packages, services, and shared libraries. AI tools try to map these dependency relationships so they can answer questions like: “If we change this library, what might break?”
In practice, dependency mapping often starts with import statements, build files, and service manifests. It gets harder with dynamic imports, reflection, or runtime wiring (common in large frameworks), so the “map” is usually best-effort—not a guarantee.
Call graphs are about execution: “who calls this function?” and “what does this function call?” This helps an AI tool avoid shallow edits that miss required updates elsewhere.
For example, renaming a method isn’t just a local change. You need to find all call sites, update tests, and ensure indirect callers (via interfaces, callbacks, or event handlers) still work.
To reason about impact, tools try to identify entry points: API routes and handlers, CLI commands, background jobs, and key UI flows.
Entry points matter because they define how users and systems reach your code. If an AI tool modifies a “leaf” function without noticing it’s on a critical request path, performance and correctness risks go up.
Data flow connects schemas, DTOs, events, and persistence layers. When AI can follow how data is shaped and stored—request payload → validation → domain model → database—it’s more likely to refactor safely (keeping migrations, serializers, and consumers in sync).
Good tools also surface hotspots: high-churn files, tightly coupled areas, and modules with long dependency chains. These are where small edits can have outsized side effects—and where you’ll want extra tests and careful review before merging.
AI can propose changes quickly, but it can’t guess your intent. The safest refactors start with a clear plan that a human can validate and that an AI can follow without improvising.
Before generating any code, decide what “done” means.
If you want a behavior change, describe the user-visible outcome (new feature, different output, new edge case handling). If it’s an internal refactor, explicitly state what must stay the same (same API responses, same database writes, same error messages, same performance envelope).
That single decision reduces accidental scope creep—where an AI “cleans up” things you didn’t ask to change.
Write constraints like non-negotiables:
Constraints act like guardrails. Without them, an AI may produce correct code that’s still unacceptable for your system.
Good acceptance criteria can be verified by tests or a reviewer without reading your mind. Aim for statements like:
If you already have CI checks, align criteria with what CI can prove (unit tests, integration tests, type checks, lint rules). If not, note which manual checks are required.
Define which files are allowed to change, and which must not (e.g., database schema, public interfaces, build scripts). Then ask the AI for small, reviewable diffs—one logical change at a time.
A practical workflow is: plan → generate minimal patch → run checks → review → repeat. This keeps refactoring safe, reversible, and easier to audit in code review.
Extending an existing system is rarely about writing purely “new” code. It’s about fitting changes into a set of conventions—naming, layering, error handling, configuration, and deployment assumptions. AI can draft code quickly, but safety comes from steering it toward established patterns and constraining what it’s allowed to introduce.
When asking an AI to implement a new feature, anchor it to a nearby example: “Implement this the same way as InvoiceService handles CreateInvoice.” This keeps naming consistent, preserves layering (controllers → services → repositories), and avoids architectural drift.
A practical workflow is to have the AI locate the closest analogous module, then generate changes in that folder only. If the codebase uses a specific style for validation, configuration, or error types, explicitly reference the existing files so the AI copies the shape, not just the intent.
Safer changes touch fewer seams. Prefer reusing existing helpers, shared utilities, and internal clients over creating new ones. Be cautious with adding new dependencies: even a small library can bring licensing, security, or build complications.
If the AI suggests “introduce a new framework” or “add a new package to simplify,” treat that as a separate proposal with its own review, not part of the feature.
For public or widely used interfaces, assume compatibility matters. Ask the AI to propose:
This keeps downstream consumers from breaking unexpectedly.
If the change affects runtime behavior, add lightweight observability: a log line at a key decision point, a counter/metric, or a feature flag for gradual rollout. When applicable, have the AI suggest where to instrument based on existing logging patterns.
Don’t bury behavior changes in a distant wiki. Update the nearest README, /docs page, or module-level documentation so future maintainers understand what changed and why. If the codebase uses “how-to” docs, add a short usage example alongside the new capability.
Refactoring with AI works best when you treat the model as a fast assistant for small, verifiable moves, not as a replacement for engineering judgment. The safest refactors are the ones you can prove didn’t change behavior.
Begin with changes that are mostly structural and easy to validate:
These are low-risk because they’re usually local and the intended outcome is clear.
A practical workflow is:
This keeps blame and rollback simple, and it prevents “diff explosions” where a single prompt touches hundreds of lines.
Refactor under existing test coverage whenever possible. If tests are missing in the area you’re touching, add a small characterization test first (capture current behavior), then refactor. AI is great at suggesting tests, but you should decide what behavior is worth locking in.
Refactors often ripple through shared pieces—common types, shared utilities, configuration, or public APIs. Before accepting an AI-generated change, scan for:
Large-scale rewrites are where AI assistance gets risky: hidden coupling, partial coverage, and missed edge cases. If you must migrate, require a proven plan (feature flags, parallel implementations, staged rollout) and keep each step independently shippable.
AI can suggest changes quickly, but the real question is whether those changes are safe. Quality gates are automated checkpoints that tell you—consistently and repeatably—if a refactor broke behavior, violated standards, or no longer ships.
Unit tests catch small behavioral breaks in individual functions or classes and are ideal for refactors that “shouldn’t change what it does.” Integration tests catch issues at boundaries (database calls, HTTP clients, queues), where refactors often change wiring or configuration. End-to-end (E2E) tests catch user-visible regressions across the full system, including routing, permissions, and UI flows.
If AI proposes a refactor that touches multiple modules, confidence should rise only if the relevant mix of unit, integration, and E2E tests still pass.
Static checks are fast and surprisingly powerful for refactoring safety:
A change that “looks fine” may still fail at compile, bundle, or deployment time. Compilation, bundling, and container builds verify the project still packages correctly, dependencies resolve, and environment assumptions didn’t change.
AI can generate tests to increase coverage or encode expected behavior, especially for edge cases. But these tests still need review: they can assert the wrong thing, mirror the bug, or miss important cases. Treat AI-written tests like any other new code.
Failing gates are useful signals. Instead of pushing harder, reduce the change size, add a targeted test, or ask the AI to explain what it touched and why. Small, verified steps beat large “one-shot” refactors.
AI can speed up edits, but it shouldn’t be the final authority. The safest teams treat the model as a junior contributor: helpful, fast, and occasionally wrong. A human-in-the-loop workflow keeps changes reviewable, reversible, and aligned with real product intent.
Ask the AI to propose a diff, not a rewrite. Small, scoped patches are easier to review and less likely to smuggle in accidental behavior changes.
A practical pattern is: one goal → one diff → run checks → review → merge. If the AI suggests touching many files, push it to justify each edit and split the work into smaller steps.
When reviewing AI-authored code, focus less on “does it compile” and more on “is it the right change.” A simple checklist:
If your team uses a standard checklist, link it in PRs (e.g., /blog/code-review-checklist).
Good prompts behave like good tickets: include constraints, examples, and guardrails.
The fastest way to create bugs is to let the AI guess. If requirements are unclear, domain rules are missing, or the change touches critical paths (payments, auth, safety), pause and get clarification—or pair with a domain expert before merging.
AI-assisted refactoring isn’t just a productivity choice—it changes your risk profile. Treat AI tools like any other third-party developer: restrict access, control data exposure, and ensure every change is auditable.
Start with the minimum permissions needed. Many workflows only require read-only access to the repository for analysis and suggestions. If you enable write access (for auto-creating branches or PRs), scope it tightly: a dedicated bot account, limited repos, protected branches, and mandatory reviews.
Codebases often contain sensitive material: API keys, internal endpoints, customer identifiers, or proprietary logic. Reduce leakage risk by:
If your tool can run generated code or tests, do it in isolated environments: ephemeral containers/VMs, no access to production networks, and tightly controlled outbound traffic. This limits damage from unsafe scripts, dependency install hooks, or accidental destructive commands.
When AI suggests “just add a package,” treat it like a normal dependency change: verify the license, security posture, maintenance status, and compatibility. Make dependency additions explicit in the PR and review them with the same rigor as code.
Keep the workflow traceable: PRs for every change, preserved review comments, and changelogs describing intent. For regulated environments, document the tool configuration (models, retention settings, access permissions) so compliance teams can verify how code was produced and approved.
AI-assisted refactors can look “clean” in a diff and still subtly change behavior. The safest teams treat every change as a measurable experiment: define what “good” looks like, compare against a baseline, and watch the system after the merge.
Before you ask an AI tool to restructure code, capture what the software currently does. That usually means:
The goal isn’t perfect coverage—it’s confidence that “before” and “after” behave the same where it matters.
Refactors can change algorithmic complexity, database query patterns, or caching behavior. If performance matters in that part of the system, keep a lightweight benchmark:
Measure before and after. If the AI suggests a new abstraction, validate that it didn’t add hidden overhead.
Even with good checks, production reveals surprises. Reduce risk with:
For the first hours/days, monitor what users would feel:
If something slips through, treat it as feedback for your AI workflow: update prompts, add a checklist item, and codify the missed scenario in a test so it can’t regress again.
Picking an AI assistant for a real codebase is less about “best model” and more about fit: what it can reliably see, change, and verify inside your workflow.
Start with concrete selection criteria tied to your repos:
It’s also worth evaluating workflow features that directly support safe iteration. For example, Koder.ai is a chat-based vibe-coding platform that emphasizes guided planning (a dedicated planning mode), controlled changes, and operational safety features like snapshots and rollback—useful when you want to iterate quickly but keep reversibility and reviewability.
Run a small pilot: one team, one service, and well-scoped tasks (feature flags, validation improvements, small refactors with tests). Treat the pilot as an experiment with clear success metrics: time saved, review effort, defect rate, and developer confidence.
Write lightweight guidelines that everyone can follow:
Integrate the tool into your CI/CD and PR flow so safety is consistent: PR templates that require a short change plan, links to test evidence, and a checklist for risky areas (migrations, permissions, external APIs).
If you want to compare options or start with a controlled trial, see /pricing.
AI “understanding” usually means it can reliably answer practical questions from what’s visible in the repo: what a function does, which modules relate to a feature, what conventions are used, and what constraints (types, tests, configs) must be respected.
It’s pattern- and constraint-matching—not human, product-level comprehension.
Because the model can only be correct about what it can see. Missing key files (configs, migrations, tests) forces it to fill gaps with guesses, which is how subtle regressions happen.
A smaller, high-quality context slice (relevant modules + conventions + tests) often beats a larger, noisier one.
Most tools prioritize source code, configs, build scripts, and infrastructure-as-code because those define how the system compiles and runs.
They often skip generated code, vendored dependencies, large binaries, or artifacts—so if behavior depends on a generation step, you may need to explicitly include or reference it.
Docs (READMEs, ADRs, design notes) explain why things are the way they are—compatibility promises, non-functional requirements, and “do not change” areas.
But docs can be stale. If you rely on them, add a quick check in your workflow: “Is this document still reflected in code/config today?”
Issue threads, PR discussions, and commit messages often reveal intent: why a dependency was pinned, why a refactor was reverted, or what edge case forced an awkward implementation.
If your assistant doesn’t ingest trackers automatically, paste the key excerpts (acceptance criteria, constraints, edge cases) directly into the prompt.
Chunking breaks the repo into usable units (files, functions, classes). Indexing builds fast lookup (keywords + semantic embeddings). Retrieval selects a small set of relevant chunks to fit into the model’s working context.
If retrieval is wrong, the model can confidently edit the wrong module—so prefer workflows where the tool shows which files/snippets it used.
Ask it to:
Then verify those claims against the repo before accepting code.
Include these in your prompt or ticket:
This prevents “helpful” but unwanted cleanup and keeps diffs reviewable.
Use an incremental loop:
If tests are weak, add a characterization test first to lock current behavior, then refactor under that safety net.
Treat the tool like a third-party contributor:
If you need team-wide rules, document them alongside your dev workflow (e.g., a PR checklist).