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    <title>DEV Community: William Daugherty</title>
    <description>The latest articles on DEV Community by William Daugherty (@willdata0101).</description>
    <link>https://dev.to/willdata0101</link>
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      <title>DEV Community: William Daugherty</title>
      <link>https://dev.to/willdata0101</link>
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      <title>Can my validation tool survive a messy retro game catalog API?</title>
      <dc:creator>William Daugherty</dc:creator>
      <pubDate>Mon, 18 May 2026 14:33:00 +0000</pubDate>
      <link>https://dev.to/willdata0101/can-my-validation-tool-survive-a-messy-retro-game-catalog-api-hf5</link>
      <guid>https://dev.to/willdata0101/can-my-validation-tool-survive-a-messy-retro-game-catalog-api-hf5</guid>
      <description>&lt;p&gt;If you’ve ever manually checked a deeply nested JSON API response at 11 PM, you know that JSON stops looking like data structures and starts looking more like ancient Sumerian. &lt;/p&gt;

&lt;p&gt;Annoying, right?&lt;/p&gt;

&lt;p&gt;So, I built &lt;code&gt;validate_me&lt;/code&gt; - an API response validator that takes a JSON response and outputs a pass/fail report with flags for missing keys or incorrect types.&lt;/p&gt;

&lt;p&gt;Let’s take a look inside this tool and highlight its most notable features.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Missing Keys Detection&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;To test &lt;code&gt;validate_me&lt;/code&gt;, I generated a deeply nested (and quite messy) JSON API response with video game data in Python. I removed several required keys from the dataset, for example, &lt;code&gt;games[0].platform&lt;/code&gt; and &lt;code&gt;games[0].developer.name&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;&lt;code&gt;validate_me&lt;/code&gt; immediately flags all those missing keys, repetitive manual validation checks, making QA workflows less error-prone and more efficient.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Nested Traversal and List Indexing&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;What about those nested path traversals? Many APIs return deeply nested JSON responses with lists within dictionaries, making validation even trickier. Check out how &lt;code&gt;validate_me&lt;/code&gt; handles these scenarios:&lt;/p&gt;

&lt;p&gt;  &lt;iframe src="https://www.youtube.com/embed/c7uFQJJWKB4"&gt;
  &lt;/iframe&gt;
&lt;/p&gt;

&lt;p&gt;&lt;code&gt;validate_me&lt;/code&gt; shines through its built-in list indexing support, making it easier to validate large, deeply nested JSON API responses. This feature assures the correct validation of arrays of objects, even in larger datasets.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Type Validation&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Did you notice it also caught incorrect types? &lt;code&gt;validate_me&lt;/code&gt; detects values that don't match the expected type. For example, &lt;code&gt;'release_year': "1992"&lt;/code&gt;, where the actual value is a string &lt;code&gt;"1992"&lt;/code&gt; instead of the expected value, an integer &lt;code&gt;1992&lt;/code&gt;. &lt;code&gt;validate_me&lt;/code&gt; flags these subtle bugs before they make it into production.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Why this matters for QA&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;If you're a QA Engineer or Software Tester, chances are your workflow could use some streamlining. &lt;code&gt;validate_me&lt;/code&gt; is a great way to automate the more tedious aspects of testing API responses in JSON, saving you time for other tasks.&lt;/p&gt;

&lt;p&gt;Here’s the GitHub repo if you’d like to check it out:&lt;/p&gt;

&lt;p&gt;&lt;a href="https://github.com/willdata0101/validate_me" rel="noopener noreferrer"&gt;https://github.com/willdata0101/validate_me&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Could you see a tool like &lt;code&gt;validate_me&lt;/code&gt; helping to streamline your QA workflows? Let me know in the comments!&lt;/p&gt;

&lt;p&gt;Thanks!&lt;br&gt;
…&lt;/p&gt;

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      <category>testing</category>
      <category>api</category>
      <category>python</category>
      <category>postman</category>
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