{"title":"Pengujian Aplikasi iOS Otomatis","slug":"automated-testing-ios","category":"guides","summary":"Gunakan Synapse + Computer Control API untuk mengotomasi pengujian aplikasi iOS via Simulator.","audience":["human","llm"],"tags":["guide","ios","testing","automation"],"difficulty":"advanced","updated":"2026-06-27","word_count":302,"read_minutes":2,"lang":"id","translated":true,"requested_lang":"id","content_markdown":"\n# Pengujian Aplikasi iOS Otomatis\n\nGabungkan sistem memori Synapse dengan Computer Control API untuk membangun\npengujian aplikasi iOS berbasis LLM. LLM mengingat skenario pengujian, belajar\ndari kegagalan masa lalu, dan beradaptasi dengan perubahan UI.\n\n## Arsitektur\n\n```\n┌──────────────┐ commands ┌──────────────┐ screenshots ┌──────────────┐\n│ LLM Agent │ ─────────────▶│ Synapse │ ────────────────▶ │ iOS Sim │\n│ (Claude) │ │ Computer │ ◀──────────────── │ (via agent) │\n└──────────────┘ │ Control │ results └──────────────┘\n │ └──────────────┘\n │ store/recall\n ▼\n┌──────────────┐\n│ Memories │ (test scenarios, past failures, UI patterns)\n└──────────────┘\n```\n\n## Prasyarat\n\n- Akun Synapse + Mind Key\n- Synapse MCP server dikonfigurasi di Claude Desktop\n- iOS Simulator dengan `screen-remote-agent` terpasang\n- Komputer terdaftar di Synapse (lihat [Computer Control API](/docs/api/computers))\n\n## Langkah 1: Daftarkan Komputer Simulator\n\nDi Mac yang menjalankan iOS Simulator:\n\n```bash\n# Get install code from Synapse\ncurl -X POST https://synapse.schaefer.zone/computers/install-code \\\n -H \"Authorization: Bearer YOUR_MIND_KEY\" \\\n -d '{\"computer_name\":\"ios-sim\"}'\n# → { \"install_code\": \"ic_...\" }\n\n# Run screen-remote-agent on the Mac\n# (uses the install code to register)\n```\n\n## Langkah 2: Simpan Skenario Pengujian di Memori\n\nSimpan skenario pengujian yang dapat digunakan kembali sebagai memori:\n\n```python\nimport requests\n\ndef store_test_scenario(name, steps, app):\n requests.post(f\"{URL}/memory\",\n headers={\"Authorization\": f\"Bearer {MIND_KEY}\"},\n json={\n \"category\": \"skill\",\n \"key\": f\"test_scenario_{name}\",\n \"content\": f\"App: {app}\\nSteps:\\n\" + \"\\n\".join(steps),\n \"tags\": [\"test\", \"ios\", app],\n \"priority\": \"high\"\n })\n\nstore_test_scenario(\"login_flow\", [\n \"Launch app\",\n \"Tap email field\",\n \"Type test@example.com\",\n \"Tap password field\",\n \"Type password123\",\n \"Tap Login button\",\n \"Verify home screen appears\"\n], \"MyApp\")\n```\n\n## Langkah 3: Eksekusi Pengujian Berbasis LLM\n\nDi Claude Desktop (dengan Synapse MCP dikonfigurasi):\n\n```\nRun the login_flow test scenario on the iOS Simulator.\nTake a screenshot after each step and verify the expected UI.\nIf any step fails, store the failure as a memory so we can\navoid it next time.\n```\n\nClaude akan:\n\n1. Memanggil `memory_search` untuk menemukan memori `test_scenario_login_flow`\n2. Memanggil `computer_screenshot` untuk melihat kondisi saat ini\n3. Mengeksekusi setiap langkah melalui `computer_command_queue` (klik, ketik)\n4. Memverifikasi hasil melalui tangkapan layar\n5. Menyimpan kegagalan apa pun sebagai memori `mistake`\n\n## Langkah 4: Pengujian Self-Healing\n\nSaat pengujian gagal, simpan kegagalan dan pemulihannya:\n\n```python\ndef store_test_failure(scenario, step, error, recovery):\n requests.post(f\"{URL}/memory\",\n headers={\"Authorization\": f\"Bearer {MIND_KEY}\"},\n json={\n \"category\": \"mistake\",\n \"key\": f\"failure_{scenario}_{step}\",\n \"content\": f\"Scenario: {scenario}\\nStep: {step}\\nError: {error}\\nRecovery: {recovery}\",\n \"tags\": [\"test\", \"failure\", \"ios\", scenario],\n \"priority\": \"high\"\n })\n\n# Example\nstore_test_failure(\"login_flow\", \"tap_login\",\n \"Login button not found at expected coordinates\",\n \"Button moved due to new logo. Search by accessibility label instead.\")\n```\n\nSaat LLM menjalankan pengujian berikutnya, ia akan recall kegagalan dan menerapkan\npemulihan secara otomatis.\n\n## Langkah 5: Pelacakan Hasil Pengujian\n\nLacak pengujian yang dijalankan sebagai tugas:\n\n```python\ndef track_test_run(scenario, status, duration):\n requests.post(f\"{URL}/mind/task\",\n headers={\"Authorization\": f\"Bearer {MIND_KEY}\",\n \"Content-Type\": \"application/json\"},\n json={\n \"title\": f\"Test: {scenario}\",\n \"description\": f\"Status: {status}, Duration: {duration}s\",\n \"priority\": \"normal\"\n })\n```\n\n## Perintah Umum\n\n| Aksi | Perintah |\n|--------|---------|\n| Luncurkan Simulator | `xcrun simctl launch booted com.example.app` |\n| Tangkapan layar | `computer_screenshot` (via Synapse MCP) |\n| Ketuk di (x,y) | `computer_command_queue {type:\"click\", payload:{x,y}}` |\n| Ketik teks | `computer_command_queue {type:\"type\", payload:{text:\"...\"}}` |\n| Tekan Home | `computer_command_queue {type:\"key\", payload:{keys:[\"Cmd\",\"Shift\",\"H\"]}}` |\n\n## Praktik Terbaik\n\n> [!TIP]\n> - **Simpan koordinat UI sebagai memori** — UI berubah, tetapi LLM dapat belajar ulang\n> - **Gunakan label aksesibilitas** — lebih stabil dari koordinat\n> - **Simpan data pengujian secara terpisah** — gunakan variabel untuk nama pengguna, kata sandi\n> - **Jalankan pengujian dalam kondisi bersih** — reset Simulator antara pengujian\n> - **Catat tangkapan layar untuk kegagalan** — berguna untuk debugging\n\n## Langkah Berikutnya\n\n- [Pengujian Self-Healing](/docs/guides/self-healing-tests)\n- [Computer Control API](/docs/api/computers)\n- [Praktik Terbaik Memori](/docs/guides/memory-best-practices)\n","content_html":"

Pengujian Aplikasi iOS Otomatis

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Gabungkan sistem memori Synapse dengan Computer Control API untuk membangun\npengujian aplikasi iOS berbasis LLM. LLM mengingat skenario pengujian, belajar\ndari kegagalan masa lalu, dan beradaptasi dengan perubahan UI.

\n

Arsitektur

\n
┌──────────────┐    commands    ┌──────────────┐    screenshots    ┌──────────────┐\n│  LLM Agent   │ ─────────────▶│  Synapse     │ ────────────────▶ │  iOS Sim     │\n│  (Claude)    │               │  Computer    │ ◀──────────────── │  (via agent) │\n└──────────────┘               │  Control     │    results        └──────────────┘\n       │                       └──────────────┘\n       │ store/recall\n       ▼\n┌──────────────┐\n│  Memories    │ (test scenarios, past failures, UI patterns)\n└──────────────┘

Prasyarat

\n\n

Langkah 1: Daftarkan Komputer Simulator

\n

Di Mac yang menjalankan iOS Simulator:

\n
# Get install code from Synapse\ncurl -X POST https://synapse.schaefer.zone/computers/install-code \\\n  -H "Authorization: Bearer YOUR_MIND_KEY" \\\n  -d '{"computer_name":"ios-sim"}'\n# → { "install_code": "ic_..." }\n\n# Run screen-remote-agent on the Mac\n# (uses the install code to register)

Langkah 2: Simpan Skenario Pengujian di Memori

\n

Simpan skenario pengujian yang dapat digunakan kembali sebagai memori:

\n
import requests\n\ndef store_test_scenario(name, steps, app):\n    requests.post(f"{URL}/memory",\n        headers={"Authorization": f"Bearer {MIND_KEY}"},\n        json={\n            "category": "skill",\n            "key": f"test_scenario_{name}",\n            "content": f"App: {app}\\nSteps:\\n" + "\\n".join(steps),\n            "tags": ["test", "ios", app],\n            "priority": "high"\n        })\n\nstore_test_scenario("login_flow", [\n    "Launch app",\n    "Tap email field",\n    "Type test@example.com",\n    "Tap password field",\n    "Type password123",\n    "Tap Login button",\n    "Verify home screen appears"\n], "MyApp")

Langkah 3: Eksekusi Pengujian Berbasis LLM

\n

Di Claude Desktop (dengan Synapse MCP dikonfigurasi):

\n
Run the login_flow test scenario on the iOS Simulator.\nTake a screenshot after each step and verify the expected UI.\nIf any step fails, store the failure as a memory so we can\navoid it next time.

Claude akan:

\n
    \n
  1. Memanggil memory_search untuk menemukan memori test_scenario_login_flow
    2. \n
  2. Memanggil computer_screenshot untuk melihat kondisi saat ini
    3. \n
  3. Mengeksekusi setiap langkah melalui computer_command_queue (klik, ketik)
    4. \n
  4. Memverifikasi hasil melalui tangkapan layar
    5. \n
  5. Menyimpan kegagalan apa pun sebagai memori mistake
    6. \n
\n

Langkah 4: Pengujian Self-Healing

\n

Saat pengujian gagal, simpan kegagalan dan pemulihannya:

\n
def store_test_failure(scenario, step, error, recovery):\n    requests.post(f"{URL}/memory",\n        headers={"Authorization": f"Bearer {MIND_KEY}"},\n        json={\n            "category": "mistake",\n            "key": f"failure_{scenario}_{step}",\n            "content": f"Scenario: {scenario}\\nStep: {step}\\nError: {error}\\nRecovery: {recovery}",\n            "tags": ["test", "failure", "ios", scenario],\n            "priority": "high"\n        })\n\n# Example\nstore_test_failure("login_flow", "tap_login",\n    "Login button not found at expected coordinates",\n    "Button moved due to new logo. Search by accessibility label instead.")

Saat LLM menjalankan pengujian berikutnya, ia akan recall kegagalan dan menerapkan\npemulihan secara otomatis.

\n

Langkah 5: Pelacakan Hasil Pengujian

\n

Lacak pengujian yang dijalankan sebagai tugas:

\n
def track_test_run(scenario, status, duration):\n    requests.post(f"{URL}/mind/task",\n        headers={"Authorization": f"Bearer {MIND_KEY}",\n                 "Content-Type": "application/json"},\n        json={\n            "title": f"Test: {scenario}",\n            "description": f"Status: {status}, Duration: {duration}s",\n            "priority": "normal"\n        })

Perintah Umum

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
AksiPerintah
Luncurkan Simulatorxcrun simctl launch booted com.example.app
Tangkapan layarcomputer_screenshot (via Synapse MCP)
Ketuk di (x,y)computer_command_queue {type:"click", payload:{x,y}}
Ketik tekscomputer_command_queue {type:"type", payload:{text:"..."}}
Tekan Homecomputer_command_queue {type:"key", payload:{keys:["Cmd","Shift","H"]}}
\n

Praktik Terbaik

\n

Langkah Berikutnya

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