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Benchmark an AI coding agent on a complete, version-pinned Python repository—not a small standalone exercise—and judge the result on more than whether visible tests pass. Give every run a realistic issue or change request, isolate the environment, and record the agent’s tool trajectory, retries, elapsed time, tokens, and infrastructure cost. Validate its patch with hidden and visible tests, regression and static checks, representative workloads, and maintainability measures. Report correctness, performance, reliability, and total cost as separate results, then break them down by repository, task family, difficulty, retrieval setup, and harness. This design measures the work that makes large repositories difficult: finding the right code, tracing dependencies, discovering tests, and changing several files without creating new failures.
Why do large Python repositories require a different benchmark?
A benchmark built from short functions mostly measures code generation. A large repository benchmark must also measure repository navigation, semantic retrieval, dependency tracing, test discovery, configuration work, and cross-file reasoning. An agent can produce locally plausible code yet fail because it edited the wrong implementation, missed a plugin registration, misunderstood a data contract, or never ran the relevant integration test.
Use a complete checkout at a known commit. The task should reference behavior in that checkout, and the evaluator should be able to run the same tests and workloads that maintainers use. Keep the repository’s normal layout, build scripts, optional components, and integration boundaries instead of flattening the task into a hand-picked file. This preserves the interaction between core libraries, command-line interfaces, services, data pipelines, and external systems.
Large-scale difficulty is not equivalent to line count. A compact change that crosses an API boundary can be harder than a large isolated refactor. Record repository size, number of files touched by the reference patch, dependency depth, test-layer involvement, and whether the task requires retrieval outside the initially named module so that difficulty can be analyzed rather than guessed.
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How should you define the unit of evaluation?
The basic unit is one agent attempt on one task tied to one repository commit. Freeze the task’s starting state and write down exactly what the agent is expected to change and how success will be judged.
Write a task contract
- Repository and commit: identify the repository revision and any submodules or generated assets required to reproduce it.
- Request: provide an issue, failing test, feature request, security fix, refactor, dependency or API migration, or performance regression in the language a maintainer would use.
- Expected behavior: state observable requirements, compatibility expectations, error handling, and performance targets. Do not reveal an implementation path unless that is part of the real issue.
- Scope and permissions: list allowed files or directories only when the real workflow imposes such limits; otherwise let the agent discover the relevant surface. Specify whether network access, package installation, shell commands, code search, and external tools are allowed.
- Starting checks: record the baseline test command, known failures, lint and type-check status, and representative workload measurements before the agent runs.
- Oracle: retain an expert reference patch or a precise behavioral oracle when available. The reference is for evaluation and analysis, not something the agent sees.
SWE-fficiency follows this complete-codebase model: agents investigate semantics, localize bottlenecks and tests, and submit a patch that meets an expert speedup target while passing the same tests. Its 2026 release contains 498 tasks across nine repositories. That grounding is stronger than asking an agent to solve an isolated algorithm with no repository context.
Capture the attempt, not just the final diff
Assign a unique run identifier and retain the initial prompt, all system and tool instructions, model and agent configuration, every tool call and output, file reads and writes, command exit codes, timestamps, retries, intermediate patches, final patch, and evaluator logs. A final diff cannot tell you whether an agent solved a task directly, spent its budget on repeated failed commands, or succeeded only after an accidental environmental change.
What task mix represents real Python maintenance?
Stratify the corpus before running agents. A single aggregate solve rate hides whether a system is good at bug fixes but poor at migrations or performance work.
| Task family | What to include | What it tests |
|---|---|---|
| Bug fixes | Reproducing defects, edge cases, and regressions with a failing test or clear report | Diagnosis, localization, and regression safety |
| Feature additions | Changes spanning public APIs, configuration, CLI behavior, or service endpoints | Cross-file design and compatibility |
| Refactors | Internal reorganizations that must preserve behavior and supported interfaces | Dependency tracing and maintainability |
| Dependency or API migrations | Version changes, deprecations, serialization changes, or adapter updates | Repository-wide search and integration work |
| Test repairs | Flaky, obsolete, incomplete, or incorrectly isolated tests | Test understanding without weakening coverage |
| Performance optimization | Measured bottlenecks with a representative workload and an expert target | Profiling, algorithmic change, and measurement discipline |
Sample across core libraries, command-line layers, services, data pipelines, storage, and integration boundaries. Include easy, medium, and high-dependency tasks, and label patch size, files changed, test layers involved, and whether the task requires a new test.
SWE-PolyBench offers a useful scale reference: its 2025 collection has 2,110 instances from 21 repositories, covering bug fixes, features, and refactoring across several languages, including Python. Because it is multi-language, its aggregate results should not be presented as a pure estimate for large Python repositories.
For performance work, use tasks with real workloads and expert patches. FormulaCode (2026) mines 957 bottlenecks from scientific Python repositories and associates each task with an average of 264.6 community-maintained workloads. Those workloads make it possible to evaluate triage, diagnosis, and resolution rather than awarding credit for a patch that merely passes a narrow unit test.
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How do you freeze the execution environment?
Reproducibility begins before the first agent call. Build an immutable image or virtual machine and make every variable that can affect the result explicit.
- Pin the source: check out the exact repository commit, submodules, generated files, and fixture data used by the task.
- Pin Python and operating-system versions: record the interpreter build, operating-system image, architecture, compiler, and important system libraries.
- Lock dependencies: use the repository’s lockfile or an export with hashes. Record optional extras, environment markers, package indexes, and any prebuilt wheels.
- Fix hardware: specify CPU and GPU class when relevant, memory, storage type, and concurrency limits. A performance result without hardware context is not comparable.
- Control services and data: provide deterministic databases, queues, network stubs, credentials with least privilege, and test datasets. Keep private tests and held-out tasks unavailable to the agent.
- Fix agent variables: record model version, context limit, temperature or other sampling settings, system prompt, tools, retrieval configuration, timeout, and budget.
- Set randomness policy: use fixed seeds where supported. For stochastic agents or workloads, run repeated trials and publish confidence intervals or another dispersion measure.
- Validate the baseline: run the evaluator before the agent and store test, lint, type, security, and workload results, including known baseline failures.
Isolation prevents an agent from changing a dependency, reading another task’s answer, or leaving state that benefits a later run. Reset the container or virtual machine for every attempt, and hash the image and lockfile so a later reproduction can verify that it used the same environment.
Which metrics should a benchmark report?
Use a scorecard with independent dimensions. A single pass rate cannot show whether a patch is fast, safe, maintainable, or expensive.
| Dimension | Metrics to record | Interpretation |
|---|---|---|
| Functional correctness | Visible tests, hidden tests, requirement checks, and regression tests | Whether the requested behavior works beyond examples shown to the agent |
| Regression safety | Unchanged tests, type checks, lint, security checks, backward-compatibility checks | Whether unrelated behavior and supported interfaces remain intact |
| Performance | Representative runtime, throughput, memory, and expert-relative speedup | Whether the change improves the target workload without shifting cost elsewhere |
| Patch quality | Changed lines and files, complexity or duplication deltas, test additions, and reviewer rubric | Whether maintainers can understand and safely evolve the patch |
| Agent process | Time to first viable patch, elapsed time, retries, tool calls, failed commands, and trajectory length | How efficiently and reliably the agent reached a candidate solution |
| Cost | Model tokens, tool and sandbox charges, retries, and total task cost | What an end-to-end run consumes, not just the model’s token price |
Report numerator and denominator for every rate, and distinguish “failed to produce a patch” from “patch produced but rejected.” Preserve partial scores for performance and quality even when functional tests fail so readers can diagnose failure modes.
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How should correctness and regression safety be tested?
Visible tests establish basic behavior; hidden tests protect against overfitting. Keep hidden tests, held-out fixtures, and selected integration scenarios inaccessible until evaluation.
Build layered checks
- Requirement tests: exercise the exact behavior described in the task, including boundary and error cases.
- Regression tests: replay the original failure and nearby historical cases.
- Unchanged-suite tests: run the repository’s existing unit, integration, CLI, and service tests that can execute in the frozen environment.
- Static checks: run the project’s formatter, linter, type checker, import-cycle or API checks, and security scanners.
- Compatibility checks: verify public signatures, serialized formats, command exit codes, configuration defaults, and supported Python versions.
- Negative checks: test malformed input, timeouts, cancellation, permission failures, and resource exhaustion where the component handles them.
Record the first failing check and the full failure log. A patch that passes visible tests but removes an assertion, broadens an exception handler, skips a test, or changes a default should be marked as a regression even if the suite is green.
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How do you evaluate performance changes?
Performance tasks need a stable workload, warm-up policy, and target metric. Run the workload several times according to a predetermined protocol, discard only explicitly defined warm-up runs, and report the distribution rather than one favorable timing.
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- Runtime or latency at the workload sizes that expose the bottleneck.
- Throughput, queue depth, or requests per second for service and pipeline tasks.
- Peak and steady-state memory, temporary storage, and network traffic when material.
- Correctness of outputs and numerical tolerances before accepting a speedup.
- Expert-relative speedup when an expert patch and target are available.
Keep the workload data and command in the task definition, but do not expose private evaluation cases. If an agent optimizes a microbenchmark while degrading a representative workload, score the regression explicitly. FormulaCode’s multi-objective approach—triage, diagnosis, and resolution—illustrates why performance should not be reduced to a binary pass.
How should retrieval and the harness be compared?
Retrieval and orchestration are experimental factors, not invisible implementation details. Hold the model, prompt, task, timeout, and evaluator constant while changing one retrieval or harness condition.
| Condition | What the agent receives | Question answered |
|---|---|---|
| Full repository | Checkout plus normal search and shell tools | How does the agent perform with unrestricted repository navigation? |
| Curated context | Selected files or symbols supplied by the harness | How much does expert context improve outcomes? |
| Search tools | Repository search with controlled indexes or commands | Does retrieval quality limit diagnosis? |
| MCP or external retrieval | Specified context servers and their permissions | What is the value and cost of an added retrieval layer? |
| No retrieval | Only the task statement and explicitly supplied files | How much work depends on repository discovery? |
CodeScaleBench (2026) compares matched tasks with and without MCP context retrieval across 20 suites, a useful pattern for isolating retrieval effects. Databricks’ internal 2026 benchmark used real engineer tasks on a multi-million-line, multi-language codebase and found that the harness dramatically affected cost and quality. Its conclusion is practical: token price alone is a poor proxy for end-to-end task cost.
Log context supplied, retrieval queries, returned documents, tool latency, truncation, and failed calls. Otherwise a “model comparison” may actually be a comparison of search indexes, context budgets, or retry policies.
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Use the same sequence for every agent and record a machine-readable event log.
- Select and stratify tasks: sample repositories, task families, difficulty bands, patch sizes, and performance requirements before seeing agent results.
- Prepare the baseline: build the pinned image, install locked dependencies, run baseline checks, and verify that the task is solvable in the stated environment.
- Assign a clean run: create a fresh isolated workspace, unique run identifier, fixed seed policy, and resource limits.
- Start telemetry: timestamp prompts, model responses, tool calls, command outputs, token counts, retries, failures, and resource consumption.
- Give the task exactly once: do not repair an ambiguous prompt for one agent. If a task is withdrawn for an objective flaw, exclude it for every system and explain why.
- Enforce stopping rules: end on a successful submission, timeout, budget exhaustion, or explicit agent termination. Preserve the last workspace and patch for diagnosis.
- Run evaluation in a clean checkout: apply only the submitted patch to a fresh copy, then execute visible, hidden, regression, static, compatibility, and workload checks in a fixed order.
- Review quality: calculate change and complexity deltas and use a blinded rubric for readability, test adequacy, error handling, and maintainability.
- Repeat where required: rerun stochastic tasks or report confidence intervals. Never average attempts with different environments or budgets as if they were one run.
- Store artifacts: retain the image digest, lockfile, task metadata, complete trajectory, patch, evaluator output, timing, tokens, and cost ledger.
How should results be reported?
Publish distributions and slices, not only an overall solve rate. At minimum, show results by repository, task family, difficulty, patch size, files changed, retrieval condition, harness, model, timeout, and cost.
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For each slice, report attempts, functional solve rate, hidden-test pass rate, regression rate, performance speedup, memory impact, patch-size and complexity changes, median and tail time, retries, tool calls, and total cost. Include uncertainty intervals for repeated stochastic runs. A dashboard should let a reader distinguish a system that solves many easy library bugs from one that handles fewer but high-impact performance or integration tasks.
SWE-PolyBench exposes language, complexity, retrieval precision and recall, and resolve-rate views; those diagnostic cuts are more informative than a leaderboard number. If you publish a composite score, define the weights and normalization before evaluating, show every component beside it, and provide an unweighted table so readers can apply a different risk preference.
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How can you detect when an agent makes the codebase worse even though tests pass?
Green tests are a gate, not a quality verdict. Examine the patch and the system-level measurements for changes that the current suite does not cover.
- Compare public API signatures, exported names, configuration defaults, schemas, and serialized formats against the baseline.
- Inspect exception handling for swallowed errors, overly broad catches, altered exit codes, and missing cleanup.
- Check concurrency, retries, transaction boundaries, and resource lifetimes under realistic load.
- Measure complexity, duplication, import relationships, and changed-line concentration. A passing patch that duplicates logic or creates a cycle raises future maintenance risk.
- Review tests added by the agent for meaningful assertions, isolation, and failure sensitivity rather than coverage padding.
- Run representative workloads to catch latency, memory, throughput, or numerical regressions absent from unit tests.
- Use security and dependency checks to detect unsafe parsing, permission changes, secret exposure, or accidental upgrades.
- Have reviewers classify findings as blocker, required change, or advisory, and publish inter-reviewer agreement when using multiple reviewers.
Keep these findings separate from functional correctness. A patch can be functionally correct but rejected for a security or maintainability regression, and that distinction is valuable when comparing agents.
What infrastructure and metadata should a benchmark retain?
A reproducible run is an auditable experiment. Retain enough information for another team to recreate the environment and understand every decision that affected the score.
Minimum metadata checklist
- Repository URL or internal identifier, commit, submodule revisions, task text, and task-family and difficulty labels.
- Python, operating-system, compiler, dependency-lock, container or VM image, and hardware identifiers.
- Model version, agent framework version, prompts, context limits, sampling settings, tool permissions, retrieval index or server version, and timeout or token budgets.
- Random seeds, number of repetitions, workload warm-up and sampling policy, dataset hashes, and service configuration.
- Baseline and evaluator commands, hidden-test policy, static-check versions, expert reference patch availability, and scoring rules.
- Per-event timestamps, token and cost records, tool inputs and outputs, exit codes, intermediate states, final patch, and complete test and workload logs.
Separate model charges from tool, compute, storage, and retry charges. This makes it possible to explain why a cheaper model in a tool-heavy harness may cost more per solved task than a higher-priced model that needs fewer calls.
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What common benchmark mistakes should you avoid?
| Mistake | Why it misleads | Fix |
|---|---|---|
| Using only tiny, single-file exercises | Removes retrieval and dependency-tracing difficulty | Use complete repositories and multi-file tasks |
| Scoring only visible tests | Rewards overfitting and test avoidance | Add hidden, regression, static, compatibility, and workload checks |
| Changing harnesses between agents | Confounds model quality with orchestration | Hold harness constant or report it as an explicit factor |
| Comparing token prices | Ignores retries, tool calls, latency, and sandbox charges | Report total task cost and its components |
| Publishing one aggregate number | Hides repository and task-family failures | Publish distributions and diagnostic slices |
| Ignoring baseline failures | Attributes pre-existing defects to the agent | Record and subtract known baseline failures using a fixed policy |
| Leaking private tests or reference patches | Turns evaluation into answer retrieval | Keep held-out assets inaccessible until scoring |
| Reporting performance without conditions | Timing varies with hardware, data, and warm-up | Pin hardware and workload protocol and publish distributions |
| Using a composite score without weights | Readers cannot interpret trade-offs | Declare weights in advance and show raw metrics |
How should the limitations be stated?
Benchmark results are conditional on the repositories, tasks, models, prices, harnesses, and versions tested. Published task counts, leaderboards, and model prices can change as benchmark releases evolve, so include the release date and exact revision in every report.
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Databricks’ harness findings come from an internal, multi-language benchmark rather than a public Python-only suite. SWE-PolyBench also spans multiple languages, so its aggregate results should not be treated as a large-Python estimate. The cited benchmark descriptions do not establish a universal quality threshold; choose acceptance criteria that match your repository’s risk, compatibility, and performance requirements.
Finally, no benchmark number replaces maintainer judgment. A reliable comparison explains what was measured, under which conditions, what failed, and how much engineering effort each successful patch consumed.
Frequently Asked Questions
How many times should I repeat an AI coding-agent task?
Repeat any task whose model sampling, retrieval, or workload is stochastic, using the same frozen environment and a predetermined repetition count. Report the number of attempts and a confidence interval or other dispersion measure instead of presenting the best run.
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Is a SWE-bench-style issue set enough for a large Python monorepo?
It can provide realistic issue wording, but a monorepo benchmark should add repository-scale retrieval, integration boundaries, hidden tests, and representative workloads. Include monorepo-specific slices so a strong result is not driven only by isolated package fixes.
What should I do when the baseline already has failing tests?
Record the exact baseline failures before any agent runs and classify them as allowed, task-related, or disqualifying. Apply the same policy to every attempt, and score newly introduced failures separately from failures that were already present.
How do I keep benchmark artifacts from becoming a security risk?
Store trajectories, patches, datasets, and credentials in access-controlled systems, redact secrets from logs, and give agents least-privilege tokens. Keep hidden tests and held-out tasks in a separate evaluator environment that the agent cannot read.
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