OrangePro

io.github.OrangeproAI由 GitHub 帳號 OrangeproAI 發布v0.2.12更新於 Oct 24, 2026

Find test gaps, generate grounded tests, and dynamically prove behavior with mutation testing.

已驗證STDIO僅桌面開發者工具安全與監控

概覽

AI 產生的概覽

本機 MCP 伺服器,將儲存庫中的函式與呼叫它們的測試對應起來,排序尚未驗證的缺口,並驗證測試是否真能抓到程式被破壞。

可以做什麼
OrangePro 分析本機儲存庫,把每個公開函式與真正呼叫它的測試關聯起來,並依風險分數排序尚未驗證的函式。它在隔離副本中執行變異驗證:替換函式內容後重新執行你自己的測試,看測試是否失敗。它會產生 HTML 報告、追溯矩陣與含測試草稿的 tests.csv,並提供 18 個 MCP 工具,例如 orangepro_start、orangepro_find_test_gaps、orangepro_prove_loop 與 orangepro_generate_tests。
適用情境
當你想知道程式庫中哪些部分真正受到測試保護、而不只是被執行時,或希望編碼代理挑選下一個要寫的測試並加以驗證時使用。適合使用 TypeScript、JavaScript、Python、Go 或 Java 且已有測試套件的儲存庫。
執行需求
macOS 或 Linux 上的 Node.js 20 或更新版本(未測試 Windows);用於變更歷史的 Git;需先安裝儲存庫自身相依套件,讓驗證能執行其測試。透過 npx 在本機執行。可選的模型金鑰:OPENAI_API_KEY、ANTHROPIC_API_KEY 或 OLLAMA_BASE_URL,僅用於產生測試。
安裝前請注意
分析、排序與驗證不需要金鑰,也不會向 OrangePro 傳送任何內容。若設定 OPENAI_API_KEY 或 ANTHROPIC_API_KEY,包含函式名稱、檔案與證據的提示會直接送往該供應商,草擬的測試會出現在報告與 tests.csv 中。驗證在隔離副本中執行你的測試,原始檔案不會被修改。報告寫入 .orangepro/ 目錄。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 OrangePro,將其新增到工作區。
  2. 為需要使用其工具的對話啟用該服務。

Desktop only,透過 STDIO。 STDIO 服務會啟動本機處理程序,因此需要 SourceWeft 桌面主機。

其他 MCP 客戶端

參照 儲存庫 中的啟動說明。

README

[OrangePro]

See which code your tests really protect. Prove it by breaking the code on purpose.

[npm version] [MIT License] [npm downloads] [Glama score] [MCP Registry]


OrangePro maps every public function in your repository, links each one to the tests that actually call it, and ranks what's left by what it can break. For a test you care about, it breaks the function in an isolated copy and checks that the test fails. It runs on your machine, uses no AI model for scoring, and sends nothing to OrangePro. Model calls happen only if you add your own key, for optional test generation and suggestions.

bash
npx -y @orangepro/mcp-server@latest start .

Contents


What you get

Every run writes two reports and a spreadsheet of tests to .orangepro/:

FileForWhat's in it
short_behavior-coverage.htmlLeads, managers, anyone in a hurryOne page: what to do with the 20 highest-priority functions, the top five with the reason and the tests drafted for each, what changed since the last report, and how to reproduce the run.
behavior-coverage.htmlDevelopersWorklist: every ranked function with what to do, filters, and a side panel with the evidence, why it ranks, two drafted tests and the proof command. Map: where tests reach, by area. Run details: inputs, counts, proofs, fingerprints and settings.
tests.csvQA, test managersOne row per test slot (preconditions, steps, test data, expected result, priority, folder), ready for Excel, Google Sheets, TestRail, Jira Xray or Zephyr Scale. Export tests (CSV) in the detailed report downloads it, and its Run details show how the columns map (Importing tests.csv).

Every function gets one of four things to do:

What it meansWhat to do
DoneA test fails when this code breaks.Nothing. It moves back to Prove if the code changes.
ProveA test runs it, or a coverage run executes it, but no test is proven to catch a break.Run the proof command.
CheckA test file or test name points here, but no call was traced.Read that test file first; prove it if it drives the function, otherwise write one.
WriteNo test catches a break here yet.Write a test (two are drafted for the top 20), then prove it.

The one-page summary

[OrangePro one-page summary: what to do with the 20 highest-priority functions, and the top five with their tests]

The detailed report

[OrangePro detailed report: the worklist with filters and the side panel for one function]

Both from OrangePro run on its own repository at commit 64a5213, without a model key, so no tests were drafted. With a key, each of the top 20 shows two drafted tests, and the side panel shows their steps and code.


Why developers use it

  • It tells you what a test actually checks, not just what it runs. A test that calls a function isn't necessarily checking it. opro prove replaces the function body with a fixed return value in an isolated copy and reruns your own test, unchanged. If the test still passes, it wasn't protecting that function.
  • It puts consequences first. One filter shows the code paths that reach a delete or purge (cache evictions don't count); others show code that changes often, has many callers or runs on a schedule.
  • It's honest about evidence. A function is "linked" only when a test calls that exact function. A test with a similar name is a lead, never coverage. Tests that only check mocks don't count.
  • It's repeatable. Same commit, full git history, same config and same version give the same ranking. Each report records fingerprints, so you can tell when a change in results came from the code and when it came from the tool.
  • Your coding agent can drive it. As an MCP server it gives Claude Code, Cursor, Copilot, Codex and others the ranked gaps, the suggested test location and the proof step in one loop.
  • It's free, local and open source (MIT). No account, no API key for analysis, and no upload.

Quick start

bash
cd /path/to/your/repo# install the repo's own dependencies first (npm ci, uv sync, go mod download, ...)npx -y @orangepro/mcp-server@latest start .open .orangepro/short_behavior-coverage.html

Analysis, ranking and proof need no model key. Test generation is optional and uses your own key (see Model setup).

Tips for the most accurate run:

  • Use a full clone, not a shallow one. Change history drives part of the ranking, and the report says when history was partial.
  • Exclude what isn't product code with rank_exclude_paths (see Configuration).
  • Rerun after a change. Each report says what changed since the last one. When nothing changed (same commit, files, settings, coverage and proofs), start reuses the last reports at once and says so; --fresh runs in full anyway.

What's written:

.orangepro/├── short_behavior-coverage.html   ← one-page summary├── behavior-coverage.html         ← detailed report: worklist, map, run details├── tests.csv                      ← drafted tests, one row per test├── report-data.json               ← the data both reports show (used to compare and reuse runs)├── run-key.json, last-start.json  ← what the last run saw, so an unchanged repo is not analysed again├── graph.json                     ← the evidence graph (deterministic)├── ledger.json                    ← proof certificates├── rtm.md                         ← traceability matrix└── config.json                    ← optional per-repo settings
orangepro_generated/               ← generated tests (only with a model key); your files are never edited

Evidence tiers

Every function gets exactly one evidence label. The four things to do above are read from it.

EvidenceWhat it meansTo do
ProvenA test passed on the original code and failed at its own assertion when this function was broken in an isolated copy.Done
Runs under testsA coverage tool you ran executed this code.Prove
LinkedA test calls this exact function. That's a structural link, not proof.Prove
UnconfirmedA test file or a test with a similar name points here, but no call was traced. That's a lead, not evidence.Check
No test foundNothing links a test to it.Write

A proof that ran and did not catch the break turns the function into Write; a proof made before the code changed turns it back into Prove.

What OrangePro doesn't count, so linked numbers are a floor, not a coverage percentage:

  • tests that only assert on mocks;
  • calls made over HTTP or from end-to-end suites;
  • in Python, objects that reach a test only through a fixture.

Check the tests before writing new ones for a flagged path.

"Proven 0" is normal on a first run. Proof runs your tests, so it happens only for the functions you choose, or within the attempt budget of opro start.


How the ranking works

Each unproven function gets an OrangePro Risk Score, ORS = P × I × D:

  • P: how likely it is to change. Change history, fan-out, new code and size.
  • I: what it can break. Incoming references, entry-point position, data sensitivity, and a floor for paths that reach a delete or purge within two calls.
  • D: how hard a break would be to notice. Evidence tier (a linked test or a coverage run makes a break easier to notice than a name match or nothing), and whether it runs unattended (jobs and schedulers).

The score sets the order of work. It is not a defect probability. The weights are fixed and no AI model is involved. The report shows the inputs behind every row.


Prove a test

bash
# Python: the mutation value is derived from the function (return annotation or its observed result)opro prove-loop --target-symbol 'sym:app/billing/invoices.py#void_invoice' \  --test 'tests/test_invoices.py::test_void_marks_invoice_void' --replacement sentinel
# TypeScript / JavaScript: give the inert body to substituteopro prove-loop --target-symbol 'sym:src/orders.service.ts#OrdersService.cancel' \  --test src/orders.service.spec.ts --replacement 'return null;'
  • Proven: the test passes on the original code and fails at its own assertion on the broken copy.
  • Not proven: the test still passes on the broken copy, so it doesn't protect that function. That's a finding too.
  • Unrunnable: setup failed. This is never counted either way.

Find weak tests without a model key:

bash
opro roast .   # passing tests whose targeted mutant still survives

Use with your coding agent

OrangePro runs as an MCP server. Add it to your client's config:

json
{  "mcpServers": {    "orangepro-local": {      "command": "npx",      "args": ["-y", "@orangepro/mcp-server@latest", "mcp"]    }  }}
ClientWhere to put it
Claude Code.mcp.json or ~/.claude.json
Cursor~/.cursor/mcp.json or Settings → MCP
VS Code / CopilotMCP settings
Codex / OpenCode / Windsurfnpx -y @orangepro/mcp-server@latest agent --client codex prints the setup

A prompt that runs the whole loop:

"Use orangepro_start, then orangepro_find_test_gaps. For the top gap, write a test at the suggested path, run it, then call orangepro_prove_loop and tell me whether it was proven."


Configuration

Optional. Put it in .orangepro/config.json in the repository, or in ~/.orangepro/config.json for defaults across repositories. Every setting that changes the ranking is shown in the report.

json
{  "classification": {    "rank_exclude_paths": ["ui/**", "docs/**", "scripts/**"],    "test_support_paths": ["internal/testutil/**"],    "destructive_sinks": ["archive*"]  },  "tuning": { "churn_window_days": 180 },  "proof": { "python_runner": "auto", "attempt_limit": 20 },  "overrides": [    { "symbol": "sym:src/legacy/shim.ts#shim", "action": "suppress", "reason": "generated shim, not product code" }  ]}
  • rank_exclude_paths removes paths from the ranking. They are still counted as behaviors.
  • destructive_sinks adds delete-like calls for your codebase.
  • Overrides (suppress, pin, reclassify) each require a reason, and the report lists them.
  • Weights and tiers can't be configured.

Language support

LanguageMap functionsLink tests that call themDraft testsMutation proof
TypeScript / JavaScript✓✓✓ Jest, Vitest, Mocha✓ Jest, Vitest, Mocha
Python✓✓✓ pytest✓ pytest
Go✓✓✓ *_test.go✓ go test
Java✓✓✓ JUnit 4 and 5✓ Maven with JUnit 5 only
Kotlin, Rust, PHP, C#, Ruby, Swift, C, C++✓name match onlynot yetnot yet
  • "Name match only" means a test file or test name that looks like the function makes it Unconfirmed (Check). No call is traced, so these languages never reach Linked or Proven today.
  • TypeScript and JavaScript are parsed with the TypeScript compiler; the other languages with tree-sitter.
  • Python proof runs pytest through the repository's own environment: an existing .venv or venv, then uv or poetry when their lock files are present, else python -m pytest. Set proof.python_runner to use your own command.
  • Java proof needs Maven (Surefire) and JUnit 5. Gradle and JUnit 4 projects are mapped and linked, but not proven yet.
  • Not traced yet, so tested code can show as Unconfirmed: calls through Go test-suite methods and helper constructors, dependency injection that is wired at runtime, reflection, and calls made over HTTP or RPC.

Requirements and limits

  • A patched Node.js: 22.23.4 or newer 22.x, 24.22.1 or newer 24.x, or 26.12.1 or newer, on macOS or Linux. Windows isn't tested: proofs use POSIX paths and symlinks.
  • Use a patched Node.js. The October 21, 2026 Node.js security releases (22.23.4, 24.22.1, 26.12.1) fix several high-severity issues, and Node.js 20 is end-of-life and gets no fix. Odd-numbered lines (23, 25) have no patched release. The engines field in package.json records this, but npm only warns about it unless you set engine-strict=true. Check your version with node -v. See the Node.js security release notice.
  • Git, for change history. Without it, or in a shallow clone, the ranking uses structure only and the report says so. A folder that isn't a git checkout always runs in full.
  • Install the repository's dependencies first (npm ci, uv sync, go mod download, mvn -q install, …). Proof runs your own tests in an isolated copy and needs them to pass there.
  • What's read: source and test files, skipping anything in .gitignore or .orangeproignore (negations with ! aren't supported), the usual build and dependency folders (node_modules, vendor, dist, build, target, coverage, .venv, …), files over 1 MB, and generated code (files marked Code generated … DO NOT EDIT). Up to 100,000 files; ORANGEPRO_MAX_FILES lowers that and ORANGEPRO_MAX_ANALYZE_MS sets a time limit for the scan.
  • Test drafting covers the 20 highest-priority functions, two tests each, in the order the worklist shows, and the reports show those 20. Drafts are kept until the function's code changes.
  • What counts as "nothing changed" for reusing a run: the commit, every modified or untracked file that git doesn't ignore, OrangePro's settings and ignore files, coverage files, the proof ledger, the start options, whether a model key is set, and the analysis version. A run whose test drafting failed on a provider error (out of credits, rate limit) runs again in full.
  • The CSV column mapping follows each tool's CSV importer (see Importing tests.csv). Check the import preview the first time.

Privacy and network use

  • Nothing about your code or your runs is sent anywhere. There is no usage telemetry.
  • Source is read in-process and never stored or uploaded. Reports and the evidence graph contain metadata, not your source.
  • Your source files are never edited. Proofs run in an isolated copy.
  • Model calls happen only if you configure a key, and they go directly from your machine to the provider you chose. Keys, the provider, its endpoint and the model are read only from your shell environment, or from the env block of your MCP client's config. OrangePro reads no .env file, so a repository you scan cannot choose where your key is sent, and OrangePro never writes keys anywhere. opro setup saves only the provider and model name.
  • With a key, the reports and tests.csv include the tests your model drafted, and the prompts include the function names, files and evidence they're based on.
  • Reports load nothing from the network. The only outbound links are ones you click.

Feedback

Reports include a Give feedback link and a This looks wrong link on each finding. Once in a while, after the findings, they also ask whether the report helped.

  • The links carry nothing about your project. The form sends only what you review and submit.
  • You stay anonymous unless you leave an email.
bash
opro feedback          # print the link and your settingsopro feedback off      # stop the "Did this help?" question (links stay)ORANGEPRO_FEEDBACK_URL=off   # hide every feedback link

The question appears at most once every 14 days (ORANGEPRO_FEEDBACK_COOLDOWN_DAYS). That preference is stored only on your machine. MCP clients get the link as optional result metadata (_meta["ai.orangepro/feedback_url"]), and your agent is never asked to prompt you for feedback.


Reference

CLI
bash
opro                          # same as opro start .opro start . --no-ai --no-auto   # analyze + reports, no model calls, no proof attemptsopro start . --fresh          # run in full even when nothing changed since the last runopro start --base main        # scope to a branch diffopro analyze                  # build the evidence graph and reportsopro gaps --limit 10          # top unproven behaviors, rankedopro prove-loop ...           # mutation proof for one function (see above)opro roast .                  # find tests whose mutant survives (no key needed)opro doctor --proof           # why top targets aren't proven yetopro generate --base main     # tests for what this branch changed (needs a model key)opro rtm                      # traceability matrixopro export                   # metadata-only evidence packopro coverage                 # find or generate runtime coverage artifactsopro feedback [on|off]        # feedback link and invitation settingopro mcp                      # run as an MCP server (stdio)

Add --json to any read command for machine output. Run opro help for every flag.

MCP tools (18)
ToolWhat it does
orangepro_startAnalyze, write both reports and tests.csv, return next actions; returns at once with unchanged_since when nothing changed (fresh forces a full run)
orangepro_analyze_sourcesBuild or refresh the evidence graph
orangepro_find_test_gapsUnproven behaviors ranked by ORS
orangepro_prove_loopSetup + mutation proof + report refresh for one behavior
orangepro_proveMutation proof only
orangepro_generate_testsGrounded tests for gaps (model key required)
orangepro_changed_impactWhat a diff touches
orangepro_statusWorkspace state without running anything
orangepro_doctorWhat evidence to add next
orangepro_graph_scoreGraph readiness (0–100)
orangepro_rtmTraceability matrix
orangepro_statsAggregate statistics
orangepro_record_runRecord a test run result
orangepro_explain_testWhy a test was generated
orangepro_export_evidence_packMetadata-only evidence pack
orangepro_update_graphIncremental graph update
orangepro_ai_linksWeak behavior→code suggestions (optional AI, never evidence)
orangepro_ai_flowsCandidate flows (optional AI, never evidence)
Coverage artifacts

Run your own unit and integration coverage first, then opro start. OrangePro ingests Go coverprofiles, lcov, coverage.py XML and JaCoCo XML, and keeps unit, integration and unclassified coverage separate. To label artifacts, add .orangepro/coverage-suites.json:

json
{  "artifacts": {    ".orangepro/coverage/unit.coverprofile": { "suite": "unit", "command": "make unit-test-coverage" },    ".orangepro/coverage/integration.coverprofile": { "suite": "integration", "command": "make integration-test-coverage" }  }}
Model setup (BYOK, only for generation)
ProviderEnvironment variable
OpenAI-compatibleOPENAI_API_KEY (optional: OPENAI_BASE_URL, OPENAI_MODEL)
AnthropicANTHROPIC_API_KEY (optional: ANTHROPIC_MODEL)
Ollama (local, no key)OLLAMA_BASE_URL (optional: OLLAMA_MODEL)

Auto-detect order: OpenAI → Ollama → Anthropic. Override with --provider and --model, or run opro setup.

  • Model output never changes an evidence tier. Only the mutation proof can mark a behavior Proven.
  • Generated tests land in orangepro_generated/ with the files and existing tests they're grounded on, and where and how to run them.
  • Drafts whose code fails the compile check are kept as plans ("Plan only"), with the reason named. Without a key, each of the top 20 shows two empty slots with a prompt you can copy into your coding agent.
Importing tests.csv

One row per test, UTF-8. The first 12 columns are for import; the rest keep the priority, evidence and proof command with each test. Priority is High for the five highest-ranked functions and Medium for the rest.

Column in tests.csvTestRailJira XrayZephyr Scale
IDTest Case Identifier(leave Key empty)
TitleTitleSummaryName
Section (a > b)Sections Hierarchy
Folder (a/b)Test Repository PathFolder
PriorityPriorityPriorityPriority (map Medium to Normal)
Test typeTypeLabelsLabels
PreconditionsPreconditions(separate issues in Xray; add to Description if you want them)Precondition
StepsManual Test Step: Action
Test dataManual Test Step: Data
Steps with dataStepsTest Script (Steps) - Step
Expected resultExpected ResultManual Test Step: Expected ResultTest Script (Steps) - Expected Result
ReferencesReferencesDescriptionObjective
  • TestRail: Import from CSV with the Test Case (Text) template, one row per test case.
  • Jira Xray: Test Case Importer; each test imports as one manual step.
  • Zephyr Scale: it has no test-data field, so Steps with data carries it.
  • Check the importer's preview before you confirm.
PR workflow
bash
opro generate --base main     # tests for what this branch changed (read-only git diff)opro generate --changed       # current branch vs its baseopro generate --pr 1234       # checks out PR #1234 (asks first; refuses on a dirty tree)

Hosted platform

This repository is the free local tool. The OrangePro platform adds:

  • a persistent graph across PRs and repositories;
  • CI gates on evidence and risk changes;
  • requirement and incident correlation;
  • team dashboards.

Contributing

bash
git clone https://github.com/OrangeproAI/orangepro-mcp.gitcd orangepro-mcp && npm ci && npm run buildnpm test

PRs welcome. Please open an issue first for large changes.


MIT License · orangepro.ai

來源:README.md,提交 98fbdb3

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版本歷史

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  1. v0.2.12最新Sep 16, 2026

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