benchmarks
A reproducible methodology for benchmarking multi-file refactors
Learn how to create a reproducible multi-file refactor benchmark to compare AI code refactoring tools using controlled tasks, baseline capture, and scoring.
A benchmark is only as credible as its method. When you're evaluating AI code refactoring tools—whether to justify budget, pick a vendor, or measure improvement over time—you need a process that produces comparable results across runs, tools and testers. The reproducible methodology outlined below gives you that: a step-by-step protocol for scoping multi-file refactor tasks, capturing baseline state, running each tool under controlled conditions, and scoring outcomes with quantitative and qualitative checks.
Multi-file refactors are ideal benchmark targets because they stress-test a tool's context window, its ability to preserve behavior across coupled modules, and its understanding of project-wide naming and dependency graphs. Single-file tweaks tell you little; a refactor touching ten or twenty files reveals whether a tool can reason about ripple effects, maintain test coverage, and avoid subtle breakage.
Define a stable task corpus
Choose a small set of refactoring tasks—three to five is enough for an initial benchmark—that reflect the work your team actually does. Real pull requests from your own repositories are better than textbook examples because they capture domain complexity, idiosyncratic naming conventions, and the tangled dependencies that trip up real tools.
For each task, write a one-paragraph brief describing the transformation (for example, "Extract a PaymentProcessor interface from three concrete payment classes, update all call sites, and preserve existing unit tests"). Archive the repository state at the commit immediately before the refactor was merged; this becomes your baseline snapshot. Tag or branch it so you can reset cleanly between runs.
Avoid tasks with external dependencies that change over time—API rate limits, third-party library updates, or database state—unless you can mock or freeze them. The goal is that anyone running your benchmark a month from now gets the same starting conditions.
Establish evaluation criteria up front
Decide what "success" means before you run the first tool. A refactor might compile and pass tests yet introduce duplication, break naming patterns, or leave dead code behind. Your rubric should cover:
| Criterion | How to measure |
|-----------|---------------|
| Functional correctness | All tests pass; manual smoke tests succeed |
| Scope completeness | The tool touched every required file and left no orphaned references |
| Code quality | Lint rules pass; no new duplications; formatting is consistent |
| Semantic preservation | Behavior is unchanged (compare outputs or traces for key flows) |
| Review burden | Hours a human reviewer would need to verify the changes |
Assign each criterion a weight or convert it to a pass/fail gate. For example, functional correctness might be binary (a single failing test disqualifies the run), while review burden is continuous (measured in person-minutes).
If your organization uses design patterns or architecture guidelines—layering rules, naming conventions, dependency constraints—encode them as linting or static-analysis checks. Tools like eslint, pylint, or custom scripts can automate much of the quality assessment.
Control the tool's inputs and environment
Each benchmark run must give the tool the same prompt, the same codebase snapshot, and the same computational budget. Variation in any of these invalidates comparison.
1. Standardize prompts. Write a fixed instruction for each task (the one-paragraph brief from your corpus) and feed it verbatim to every tool. Do not rephrase or add clarifying hints partway through unless you document the intervention and repeat it identically for all tools.
2. Reset to baseline. Before each run, delete the working directory and restore from your tagged snapshot. Do not carry forward partial edits or configuration changes.
3. Cap iterations. If the tool is interactive, decide in advance how many back-and-forth rounds you will allow (for instance, three refinement cycles or a ten-minute wall-clock limit). If it is autonomous, set a token budget or timeout.
4. Log everything. Capture the tool's full output—code diffs, intermediate reasoning traces, error messages, and any human interventions. Version-control the logs alongside the benchmark corpus.
For AI-powered tools, note the model version and temperature setting. A tool using GPT-4o on Monday and GPT-4.5 on Tuesday is not a reproducible benchmark.
Run multiple trials per tool
A single run can be skewed by a lucky guess or an unlucky hallucination. Run each task at least three times with each tool, resetting to baseline between trials. Aggregate the results—median pass rate, mean review burden, best and worst outcomes—and report confidence intervals if your sample size supports it.
If variance across trials is high, that itself is a signal: a tool with 100% correctness on two runs and 40% on the third is less predictable than one that hits 80% every time.
Score and synthesize results
Compile your criteria into a scorecard. For a worked example, suppose you ran three tools on five tasks with three trials each (45 total runs). Your summary table might look like:
| Tool | Tasks fully correct (%) | Avg. files touched correctly | Avg. review minutes | Lint violations introduced |
|------|------------------------|------------------------------|---------------------|----------------------------|
| Tool A | 73 | 18.2 / 20 | 22 | 3.1 |
| Tool B | 87 | 19.4 / 20 | 15 | 0.8 |
| Tool C | 60 | 16.9 / 20 | 31 | 5.4 |
(These are illustrative example numbers.)
Supplement the table with qualitative notes: Did one tool consistently miss a certain type of file (like test utilities)? Did another introduce subtle type errors that slipped past CI but would have caused runtime failures?
If you're evaluating Goatfied's BUILD suite alongside other code editors, run the same benchmark on cloud coding agents that test their own changes and open pull requests. The same reproducible method applies—just add agent-specific metrics like whether the agent opened a passing PR on the first attempt or required human nudges.
Document and share your methodology
Publish your task corpus, prompts, evaluation scripts and rubric in a public or internal repository. Include setup instructions so another engineer can clone the repo, run ./benchmark.sh, and reproduce your results in under an hour. This transparency builds trust and lets others extend your work with new tools or new tasks.
Version your methodology. If you refine a prompt, tighten a scoring rule, or add a new task, bump a version number and re-run the entire benchmark. Never silently update criteria midstream.
Iterate and refine over time
No benchmark is perfect on the first pass. After your initial run, ask: Did any task prove too easy or too hard? Did your rubric miss an important quality dimension? Did you discover a confounding variable—like the tool's behavior changing based on file order—that you need to control in the next iteration?
Treat the benchmark itself as a living artifact. As your codebase evolves and new refactoring patterns emerge, retire stale tasks and add fresh ones. Re-run periodically—quarterly is a reasonable cadence—to track tool improvements and catch regressions.
Frequently asked questions
How many tasks do I need for a statistically valid benchmark?
Three to five tasks with three trials each (9–15 runs per tool) is enough to spot large differences and identify patterns. For production vendor decisions, aim for ten tasks to reduce the influence of any single outlier.
Should I include trivial refactors like renaming a variable?
No. Trivial tasks tell you whether a tool can execute basic search-and-replace, which every modern editor handles. Focus on multi-file, semantically rich transformations that require understanding call graphs and domain logic.
Can I use open-source repositories instead of internal code?
Yes, if they match your domain and complexity. Popular open-source projects are easier to share and reproduce, but they may not reflect your team's architecture patterns or testing rigor. A mix of both is ideal.
How do I handle tools that require different input formats?
Adapt the prompt syntax to each tool's interface (CLI flags, configuration files, chat messages) but keep the semantic content identical. Document every adaptation so reviewers can verify equivalence.
What if a tool performs well on benchmarks but poorly in production?
Benchmarks measure capability under controlled conditions; they do not replace live pilot testing. Use benchmark results to shortlist candidates, then run a two-week pilot with real team members on real work to assess usability, integration friction, and edge cases the benchmark did not cover.