{"id":6390,"date":"2026-06-08T06:36:27","date_gmt":"2026-06-08T06:36:27","guid":{"rendered":"https:\/\/qyrus.com\/qapi\/?p=6390"},"modified":"2026-06-08T06:36:27","modified_gmt":"2026-06-08T06:36:27","slug":"the-7-api-testing-mistakes","status":"publish","type":"post","link":"https:\/\/qyrus.com\/qapi\/the-7-api-testing-mistakes\/","title":{"rendered":"The 7 API Testing Mistakes That Keep Developers Up at 3 AM\u00a0"},"content":{"rendered":"\t\t
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“Payment API down.”<\/p>

“Users can’t log in.”<\/p>

“Checkout flow broken.”<\/p>

This is not a good notification to have once you\u2019ve built it as a developer or once you\u2019ve invested in the software as a business owner. So where do things go wrong in API testing<\/a>, and what are the specific mistakes that teams fall into and make product and technology miserable?\u00a0<\/p>

We\u2019ve\u00a0created this blog to help you understand the mistakes you make\u00a0and how to avoid them\u00a0in the long run\u00a0when dealing with complex API ecosystems and\u00a0API testing scenarios.\u00a0<\/p>

Because\u00a0we’ve\u00a0been\u00a0interacting with multiple\u00a0developers. And after hundreds of conversations with engineering teams over the past five years,\u00a0we\u2019ve\u00a0discovered something surprising:\u00a0we’re\u00a0all making the same seven mistakes<\/b>.\u00a0<\/p>

Mistake #1: Testing Endpoints in Isolation (Instead of Testing Workflows)\u00a0<\/h2>

You’ve\u00a0got Postman for manual testing, a custom script for CI\/CD, and\u00a0maybe Swagger\u00a0for documentation. Each tool tests individual endpoints beautifully. Every test passes. Ship it, right?\u00a0<\/p>

The problem that we tend to miss here is:\u00a0Real users\u00a0don’t\u00a0call one endpoint at a time. They\u00a0create\u00a0workflows:\u00a0<\/p>

  1. Create account \u2192 2. Verify email (background job + webhook) \u2192 3. Set up profile \u2192 4. Upload avatar \u2192 5. Add payment method\u00a0<\/li><\/ol>

    Somewhere between step 2 and 3,\u00a0there’s\u00a0a race condition. Step 4 has a file size limit that only appears with real images. Step 5 fails when certain payment methods are used together.\u00a0<\/p>

    Your isolated endpoint tests caught none of this because\u00a0they\u00a0weren’t\u00a0designed to test workflows\u2014they test components<\/b>.\u00a0<\/p>

    The real problem: Tool fragmentation makes this worse<\/b>\u00a0<\/p>

    Most teams\u00a0we\u00a0talk to have this setup:\u00a0<\/p>

    \u2022\u00a0\u00a0Postman<\/b>\u00a0for manual API testing\u00a0<\/p>

    \u2022\u00a0\u00a0JMeter<\/b>\u00a0or\u00a0k6<\/b>\u00a0for load testing\u00a0<\/p>

    \u2022\u00a0\u00a0Custom scripts<\/b>\u00a0for CI\/CD automation\u00a0<\/p>

    \u2022\u00a0\u00a0Swagger\/OpenAPI<\/b>\u00a0for documentation\u00a0<\/p>

    \u2022\u00a0\u00a0cURL<\/b>\u00a0commands in runbooks\u00a0<\/p>

    \u2022\u00a0\u00a0Separate security scanning tool<\/b>\u00a0<\/p>

    Each tool knows about one piece of your API. None of them understand your complete user workflows.\u00a0<\/p>

    The fix\u00a0that we suggest and works best:<\/b>\u00a0<\/p>

    Test complete workflows as one complete\u00a0unit. This means finding a tool or approach that can:\u00a0<\/p>

    \u2022\u00a0\u00a0<\/b>Chain multiple API calls in sequence\u00a0<\/p>

    \u2022\u00a0\u00a0<\/b>Validate state changes across steps\u00a0<\/p>

    \u2022\u00a0\u00a0<\/b>Handle async operations (webhooks, background jobs)\u00a0<\/p>

    \u2022\u00a0\u00a0<\/b>Test with realistic timing between steps\u00a0<\/p>

    \u2022\u00a0\u00a0<\/b>Verify the complete journey, not just individual stops\u00a0<\/p>

    This is where tools designed for workflow testing make a difference. Instead of manually chaining requests in Postman or writing complex scripts, platforms like\u00a0qAPI\u00a0let you define complete workflows with proper assertions at each step\u2014including waiting for webhooks and validating state transitions.\u00a0<\/p>

    Mistake #2: Using Admin Tokens for Everything\u00a0\u00a0<\/h2>

    You set up one test token with full admin access. Your Postman collections use it. Your automated tests use it. Your load testing scripts use it. Coverage looks great. Everything works.\u00a0<\/p>

    Why it fails in production:<\/b>\u00a0<\/p>

    Real users have constrained permissions:\u00a0<\/p>

    \u2022\u00a0\u00a0<\/b>Basic users can only access their own data\u00a0<\/p>

    \u2022\u00a0\u00a0<\/b>Support agents can view but not modify\u00a0<\/p>

    \u2022\u00a0\u00a0<\/b>Premium users have\u00a0additional\u00a0endpoints\u00a0<\/p>

    \u2022\u00a0\u00a0<\/b>Expired trial users lose access mid-session\u00a0<\/p>

    Your tests with\u00a0god-mode tokens never\u00a0validated\u00a0any of this.\u00a0<\/p>

    We’ve\u00a0seen this exact scenario play out: A team ships a feature that works perfectly for admins. Regular users get 403 Forbidden errors on every request. The feature was completely unusable for 95% of the user base. Tests? All green.\u00a0<\/p>

    The tool\u00a0spread\u00a0problem:<\/b>\u00a0<\/p>

    Here’s\u00a0how this typically breaks down:\u00a0<\/p>

    \u2022\u00a0\u00a0<\/b>Manual testing in Postman uses your personal admin account\u00a0<\/p>

    \u2022\u00a0\u00a0<\/b>Automated CI\/CD tests use a service account (also admin)\u00a0<\/p>

    \u2022\u00a0\u00a0<\/b>Load testing scripts use a single test user (you guessed it\u2014admin)\u00a0<\/p>

    \u2022\u00a0\u00a0<\/b>Security scans run as anonymous or admin\u00a0<\/p>

    \u2022\u00a0\u00a0<\/b>Nobody\u00a0actually tests\u00a0as a regular user with real constraints\u00a0<\/p>

    Each tool\u00a0operates\u00a0independently, and they all default to the path of least resistance: admin access.\u00a0<\/p>

    The fix:<\/b>\u00a0<\/p>

    Create a permission matrix and test systematically across all user roles:\u00a0<\/p>

    Roles to test:<\/b>\u00a0<\/p>

    \u2022\u00a0\u00a0<\/b>Anonymous (no token)\u00a0<\/p>

    \u2022\u00a0\u00a0<\/b>Basic authenticated user\u00a0<\/p>

    \u2022\u00a0\u00a0<\/b>Premium\/paid user\u00a0<\/p>

    \u2022\u00a0\u00a0<\/b>Support agent (read-only)\u00a0<\/p>

    \u2022\u00a0\u00a0<\/b>Admin user\u00a0<\/p>

    \u2022\u00a0\u00a0<\/b>Expired trial user\u00a0<\/p>

    \u2022\u00a0\u00a0<\/b>Suspended user\u00a0<\/p>

    What to\u00a0validate:<\/b>\u00a0<\/p>

    \u2022\u00a0\u00a0<\/b>Can users access only their own data?\u00a0<\/p>

    \u2022\u00a0\u00a0<\/b>Do premium features properly gate access?\u00a0<\/p>

    \u2022\u00a0\u00a0<\/b>Can support agents view but not\u00a0modify?\u00a0<\/p>

    \u2022\u00a0\u00a0<\/b>Do expired users get proper error messages?\u00a0<\/p>

    \u2022\u00a0\u00a0<\/b>Are admin-only endpoints\u00a0actually protected?\u00a0<\/p>

    Verify that basic users truly\u00a0can’t\u00a0access other users’ data, premium features are properly gated, support agents\u00a0can’t\u00a0modify\u00a0records, and admin-only endpoints are\u00a0actually protected.\u00a0<\/p>

    The challenge is\u00a0maintaining\u00a0different authentication tokens across different test scenarios.\u00a0qAPI\u00a0handles this by letting you define user roles once and automatically apply the right permissions across all test cases\u2014no manual token management in every test.\u00a0<\/p>

    Mistake #3:\u00a0Not\u00a0Testing\u00a0With\u00a0Real\u00a0Data\u00a0<\/h2>

    If\u00a0<\/b>your test data is clean. Simple. ASCII characters. Perfectly formed. Whether\u00a0it’s\u00a0in your Postman examples, your test scripts, or your documentation\u2014everything is sanitized and ideal.\u00a0Then you are closer to breakdown than you realize.\u00a0<\/p>

    Real users bring\u00a0new:\u00a0<\/p>

    \u2022\u00a0\u00a0<\/b>Unicode characters (Chinese names, Arabic text, emoji in bios)\u00a0<\/p>

    \u2022\u00a0\u00a0<\/b>SQL injection attempts (malicious or accidental)\u00a0<\/p>

    \u2022\u00a0\u00a0<\/b>Null values where you expected strings\u00a0<\/p>

    \u2022\u00a0\u00a0<\/b>Strings where you expected numbers\u00a0<\/p>

    \u2022\u00a0\u00a0<\/b>Empty strings, excessive whitespace, special characters\u00a0<\/p>

    \u2022\u00a0\u00a0<\/b>Edge cases you never imagined\u00a0<\/p>

    Here\u2019s\u00a0what we mean:\u00a0<\/p>

    \u2022\u00a0\u00a0<\/b>Email:\u00a0jos\u00e9.garc\u00eda@empresa.mx (special characters)\u00a0<\/p>

    \u2022\u00a0\u00a0<\/b>Name: O’Brien (apostrophe breaks queries)\u00a0<\/p>

    \u2022\u00a0\u00a0<\/b>Age: -5 (negative number)\u00a0<\/p>

    \u2022\u00a0\u00a0<\/b>Bio: Robert’); DROP TABLE\u00a0users;–\u00a0(SQL injection)\u00a0<\/p>

    \u2022\u00a0\u00a0<\/b>Phone: +1 (555) 123-4567 ext. 890 (formatting chaos)\u00a0<\/p>

    The tool nightmare:<\/b>\u00a0<\/p>

    Here’s where multiple tools make this problem exponentially worse:\u00a0<\/p>

    In Postman:<\/b>\u00a0You manually create 5-10 example requests with clean data\u00a0In your CI\/CD scripts:<\/b>\u00a0You hardcode a few test users\u00a0In your load testing:<\/b>\u00a0You generate random data\u00a0that’s\u00a0still too perfect\u00a0In your documentation:<\/b>\u00a0You show idealized examples\u00a0<\/p>

    Nobody is systematically testing the\u00a0messy, real-world data<\/b>\u00a0that\u00a0actually breaks\u00a0things.\u00a0<\/p>

    And when you have test data scattered across multiple tools, updating it becomes impossible. Found a new edge case? Now you need to:\u00a0<\/p>

    1. Add it to your Postman collection\u00a0<\/li>
    2. Update your automated test fixtures\u00a0<\/li>
    3. Modify your load testing data generators\u00a0<\/li>
    4. Remember to update documentation examples\u00a0<\/li><\/ol>

      Most teams give up after step 1.\u00a0<\/p>

      Here\u2019s\u00a0what we suggest<\/b>\u00a0<\/p>

      Adopt\u00a0a\u00a0data-driven testing with comprehensive scenarios:\u00a0<\/p>

      Instead of writing 100 individual test cases with hardcoded data,\u00a0start by defining\u00a0your test logic once and feed it different data scenarios. One test\u00a0validates\u00a0user creation; a CSV file\u00a0contains\u00a0100 different user data scenarios.\u00a0<\/p>

      This is exactly what data-driven testing in\u00a0qAPI\u00a0enables\u2014write the test once, provide a data file, and automatically run all scenarios. Adding a new edge case means adding one line to your data file, not rewriting tests.\u00a0<\/p>

      Mistake #4: Ignoring Load\u00a0Behavior\u00a0\u00a0<\/h2>

      If your API responds in 150ms during testing.\u00a0And you ship confidently. You might have even run some load tests with JMeter or k6.\u00a0<\/p>

      What we predict will happen in most times<\/b>\u00a0<\/p>

      At 100 concurrent real users:\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Database connection pool exhausts\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Memory usage spikes\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Response times jump to 8 seconds\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Cascading failures begin\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Everything crashes\u00a0<\/p>

      Your load tests completely missed this because they simulated robots, not humans.\u00a0<\/p>

      Most teams have separate tools for\u00a0different types\u00a0of testing:\u00a0<\/p>

      Functional testing:<\/b>\u00a0Postman or custom scripts (tests correctness)\u00a0Load testing:<\/b>\u00a0JMeter, k6, Gatling (tests performance)\u00a0Monitoring:<\/b>\u00a0Datadog, New Relic (tracks production)\u00a0<\/p>

      The problem?\u00a0Load testing tools\u00a0don’t\u00a0understand how real users behave<\/b>.\u00a0<\/p>

      How traditional load testing fails:<\/b>\u00a0<\/p>

      JMeter\/k6 simulation:<\/b>\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>1,000 virtual users\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Each sends requests every 2 seconds\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Constant, uniform load\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Runs for 10 minutes\u00a0<\/p>

      This simulates a DDoS attack, not actual user\u00a0behavior.\u00a0<\/p>

      Real user\u00a0behavior:<\/b>\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Browse product page (30 seconds, no requests)\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Click “Add to Cart” (1 request)\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Read reviews (2 minutes, 3-4 lazy-loaded requests)\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Hesitate at checkout (1 minute, no requests)\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Complete purchase (burst of 5-7 requests)\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Abandon site (zero requests for hours)\u00a0<\/p>

      The critical difference:<\/b>\u00a0Real users are idle 70-80% of the time, then create bursts of activity. This “bursty”\u00a0behavior\u00a0creates entirely different bottlenecks than constant load.\u00a0<\/p>

      What happens with realistic load:<\/b>\u00a0<\/p>

      When you test with realistic user\u00a0behavior\u00a0patterns, you discover:\u00a0<\/p>

      \u2022\u00a0\u00a0Connection pool exhaustion during bursts<\/b>\u00a0(not constant usage)\u00a0<\/p>

      \u2022\u00a0\u00a0Memory leaks that only surface during idle periods<\/b>\u00a0(garbage collection issues)\u00a0<\/p>

      \u2022\u00a0\u00a0Race conditions when users resume activity<\/b>\u00a0(state synchronization)\u00a0<\/p>

      \u2022\u00a0\u00a0Cache stampede during simultaneous requests<\/b>\u00a0(everyone hits checkout at once)\u00a0<\/p>

      \u2022\u00a0\u00a0Database query performance under realistic patterns<\/b>\u00a0(not just sustained load)\u00a0<\/p>

      The tool consolidation problem:<\/b>\u00a0<\/p>

      When load testing is\u00a0a completely separate\u00a0tool from functional testing:\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Load tests\u00a0can’t\u00a0validate\u00a0business logic (just HTTP status codes)\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>You’re\u00a0testing different workflows in different tools\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Bugs found in load tests require reproduction in functional tests\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>No unified view of\u00a0what’s\u00a0actually breaking\u00a0under load\u00a0<\/p>

      The\u00a0solution:<\/b>\u00a0<\/p>

      Test with realistic virtual user patterns. Real users are idle 70-80% of the time, browse for 30 seconds, make a request, wait 2 minutes reading content, then act again.\u00a0<\/p>

      This “bursty”\u00a0behavior\u00a0creates entirely different bottlenecks than constant load:\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Connection pool exhaustion during bursts (not constant usage)\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Memory leaks surfacing during idle periods\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Race conditions when users resume activity\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Cache stampedes during simultaneous checkout\u00a0<\/p>

      What to measure:<\/b>\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>p95 and p99 latency (not averages\u2014those hide pain)\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Error rates under realistic load patterns\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Resource\u00a0utilization\u00a0(CPU, memory, connections)\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Degradation curves (how performance declines)\u00a0<\/p>

      The problem with most load testing tools is they simulate robots, not humans.\u00a0qAPI’s\u00a0virtual user balance feature simulates realistic\u00a0behavior\u2014idle time, browsing patterns, abandonment rates\u2014revealing bottlenecks that uniform load testing completely misses.\u00a0<\/p>

      Mistake #5: Mocking Everything\u00a0\u00a0<\/h2>

      What it looks like:<\/b>\u00a0<\/p>

      Your test suite mocks out every external dependency:\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Mock the database\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Mock the payment processor\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Mock the email service\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Mock the external APIs\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Mock the authentication service\u00a0<\/p>

      Tests run in 0.02 seconds. Everything passes. You feel productive.\u00a0<\/p>

      Why it fails in production:<\/b>\u00a0<\/p>

      Your mocks assumed:\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Payment API returns within 2 seconds\u00a0(real: 15 seconds during Black Friday)<\/i>\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Database queries never timeout\u00a0(real: happens under load)<\/i>\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>External API always returns expected format\u00a0(real: they changed their schema yesterday)<\/i>\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Email service never fails\u00a0(real: rate limiting kicks in at 100 emails\/hour)<\/i>\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Third-party services behave like your documentation says\u00a0(real: reality is messier)<\/i>\u00a0<\/p>

      The multi-tool mocking disaster:<\/b>\u00a0<\/p>

      Here’s how mocking typically manifests across tools:\u00a0<\/p>

      In Postman:<\/b>\u00a0You test against mock servers with perfect responses\u00a0In unit tests:<\/b>\u00a0Everything is mocked for speed\u00a0In integration tests:<\/b>\u00a0Some things mocked, some real (inconsistent)\u00a0In staging:<\/b>\u00a0Different mocks than production\u00a0In production:<\/b>\u00a0No mocks, everything breaks\u00a0<\/p>

      What you see here is\u00a0that,\u00a0each environment has different assumptions about\u00a0what’s\u00a0mocked and\u00a0what’s\u00a0real. Nobody has a complete picture of what\u00a0actually works\u00a0when integrated.\u00a0This is\u00a0a serious problem\u00a0that teams choose to ignore or\u00a0miss it unintentionally.\u00a0<\/p>

      The\u00a0solution that we suggest:<\/b>\u00a0<\/p>

      Mock judiciously. Mock third-party services during fast unit\u00a0tests, but\u00a0test real integrations comprehensively.\u00a0<\/p>

      When to mock:<\/b>\u00a0Services you\u00a0don’t\u00a0control (during development), expensive operations, actions with side effects.\u00a0When NOT to mock:<\/b>\u00a0Your own database, service-to-service APIs you control, authentication flows, critical integrations\u00a0<\/p>

      Most services provide test modes: Stripe test cards, SendGrid sandbox mode, Auth0 test tenants. Use these instead of mocks\u2014they behave like production without real side effects.\u00a0<\/p>

      When your testing platform supports both quick mocked tests for development and comprehensive integration tests for CI\/CD using the same test definitions, you get the best of both worlds.\u00a0qAPI\u00a0<\/a>lets\u00a0you toggle between mock mode and real integration testing without rewriting tests.\u00a0<\/p>

      Final Thoughts: Less Tools, Better Testing\u00a0<\/h2>

      The dirty secret of modern software development:\u00a0More testing tools\u00a0doesn’t\u00a0mean better testing. Usually, it means\u00a0more time spent handling tools and\u00a0testing with higher maintenance costs.<\/b>\u00a0<\/p>

      I learned this the hard way after\u00a0maintaining\u00a0an 8-tool API testing stack that:\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Cost us $50,000+ annually in licenses and infrastructure\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Required 30% of QA time just for maintenance\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Still let critical bugs reach production\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Created so much friction that developers avoided writing tests\u00a0<\/p>

      After\u00a0consolidating\u00a0to a unified API testing platform, we:\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Cut testing tool costs by 60%\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Reduced test maintenance time by 80%\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Increased test coverage by 3x\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Actually\u00a0caught issues before production\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Made developers\u00a0want<\/i>\u00a0to write tests (because it’s not painful)\u00a0<\/p>

      The lesson:<\/b>\u00a0Invest in capabilities, not tool count.\u00a0<\/p>

      If\u00a0you’re\u00a0starting from scratch,\u00a0don’t\u00a0replicate the fragmented approach. Find a platform that covers your needs comprehensively.\u00a0<\/p>

      If\u00a0you’re\u00a0drowning in tools, audit ruthlessly:\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Which tools are\u00a0actually used\u00a0vs. gathering dust?\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Which capabilities overlap between tools?\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>What consolidation would\u00a0eliminate\u00a0the most friction?\u00a0<\/p>

      \u2022\u00a0\u00a0<\/b>Can one better tool replace three mediocre ones?\u00a0<\/p>

      Testing\u00a0isn’t\u00a0about having every tool.\u00a0It’s\u00a0about systematically\u00a0validating\u00a0that your APIs work for real users in real conditions.<\/b>\u00a0<\/p>

      Get that right\u2014with as few tools as possible\u2014and\u00a0you’ll\u00a0finally sleep through the night.\u00a0<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

      “Payment API down.” “Users can’t log in.” “Checkout flow broken.” This is not a good notification to have once you\u2019ve built it as a developer or once you\u2019ve invested in the software as a business owner. So where do things go wrong in API testing, and what are the specific mistakes that teams fall into…<\/p>\n","protected":false},"author":9,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"categories":[17,10],"tags":[],"class_list":["post-6390","post","type-post","status-publish","format-standard","hentry","category-blog","category-resources"],"acf":[],"yoast_head":"\nThe 7 API Testing Mistakes That Keep Developers Up at 3 AM\u00a0 - qAPI<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/qyrus.com\/qapi\/the-7-api-testing-mistakes\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The 7 API Testing Mistakes That Keep Developers Up at 3 AM\u00a0 - qAPI\" \/>\n<meta property=\"og:description\" content=\"“Payment API down.” “Users can’t log in.” “Checkout flow broken.” This is not a good notification to have once you\u2019ve built it as a developer or once you\u2019ve invested in the software as a business owner. 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