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						</div><p>JSON (JavaScript Object Notation) is a fundamental component of modern web development. Its simplicity and readability have made it a universal data interchange format, used across a wide range of industries and applications. The straightforward structure of JSON, which is both human-readable and machine-parseable, has contributed to its widespread adoption. However, while JSON&#8217;s structure is generally simple, adding templates to JSON data introduces a level of complexity that can be challenging to manage. The main difficulty lies in JSON&#8217;s hierarchical and nested nature, which makes it difficult to determine the depth of the structure and when to stop parsing.</p>
<p>At <a href="https://keploy.io/">Keploy</a>, an open-source testing platform, we encountered this challenge while developing our <a href="https://keploy.io/docs/advanced/test-templatisation">templatization feature</a>, which is crucial for our test automation tool. Our primary goal was to enhance the <a href="https://keploy.io/docs/features/rerecord">rerecord feature</a>—a function that allows users to rerecord a particular test set. Users had been experiencing issues when their test cases involved dynamic fields such as JWT tokens, timestamps, or other data generated from POST requests. These dynamic fields, when reused in subsequent requests, often led to test case failures because their values were not automatically updated.</p>
<p>The purpose of our templatization feature was to address this problem by dynamically identifying and replacing these fields with templates. This way, when the rerecord or <a href="https://keploy.io/docs/features/testing">test mode</a> runs, any changes in these fields would automatically propagate through all subsequent requests, ensuring that test cases remain consistent and up-to-date.</p>
<h2>The Complexity of JSON Templatization</h2>
<p>JSON&#8217;s hierarchical structure allows for the nesting of objects and arrays to any depth. While this flexibility is one of the reasons for JSON&#8217;s popularity, it also introduces significant complexity when attempting to manipulate or templatize the data it contains.</p>
<p>If you&#8217;ve ever tried to <a href="https://keploy.io/blogs/comparing-json-objects/">compare JSON</a> from two different API responses, you know that nested structures make it hard. In a flat structure, adding templates might be as simple as performing a search-and-replace operation. However, with JSON, you cannot predict the depth beforehand. You need a method that can intelligently traverse the entire structure and apply templates wherever necessary.</p>
<p>Additionally, JSON objects can contain a variety of data types, including strings, numbers, arrays, and other objects. Each type may require a different strategy for templatization. For example, a string might be replaced directly with a template, while an array of objects might require iterating over each element and selectively applying templates.</p>
<h2>Keploy&#8217;s Solution: Harnessing the Power of Recursion</h2>
<p>To tackle this problem, we turned to one of the most fundamental techniques in computer science—<strong>recursion</strong>. Recursion is a method in which a function calls itself to break down a problem into smaller, more manageable parts. This approach is particularly well-suited for problems that involve nested or hierarchical structures, like JSON.</p>
<h3>1. Identifying Noisy Fields</h3>
<p>The first step in our approach was to identify &quot;noisy&quot; fields within JSON responses—fields that change frequently between requests, like JWT tokens or timestamps. These fields are prime candidates for templatization because their dynamic nature can otherwise cause test failures.</p>
<p>For instance, consider a JWT token generated for each user session. If this token changes but is not updated in the test cases, the tests will fail. By identifying these fields and replacing them with templates, <a href="https://keploy.io/">Keploy</a> ensures that tests remain valid even when data changes.</p>
<h3>2. Developing the Recursive Function</h3>
<p>The core of our solution was a <strong>recursive function</strong> capable of traversing the entire JSON structure, no matter how deeply nested it was.</p>
<p>Whenever we encountered a nested object or array, our function would call itself recursively to manage it. This allowed us to templatize <strong>any field at any level</strong> while maintaining JSON structure integrity.</p>
<p>In case you&#8217;re wondering <a href="https://keploy.io/docs/cli/commands#open-a-json-test-case">how to open a JSON file</a> to inspect it manually or through automation, Keploy provides CLI tools to manage and interact with recorded test cases easily.</p>
<h3>3. Applying Templates</h3>
<p>Once the noisy fields were identified, the function replaced them with templates—like replacing a JWT token with <code>{{jwt_token}}</code>.</p>
<p>This templating ensures that during test reruns, Keploy automatically fills these placeholders with fresh values, preventing stale data issues and test flakiness.</p>
<h3>4. Updating Mechanism</h3>
<p>A key advantage of our recursive approach is its <strong>dynamic updating mechanism</strong>.</p>
<p>When running rerecords or tests, any changes detected are automatically reflected across all related requests. This ensures test case consistency without manual intervention.</p>
<p>If you’ve ever needed to <a href="https://keploy.io/blogs/handling-escaped-json-in-tests/">unescape JSON</a> (e.g., turning escaped strings back into readable format), you&#8217;ll understand why maintaining clean, structured, and template-filled JSON is critical in automation pipelines like Keploy&#8217;s.</p>
<h2>Real-World Application of Recursion</h2>
<p>Imagine receiving an API response with nested user profiles, authentication tokens, and session metadata.</p>
<p>Using our recursive function, Keploy templatizes these noisy fields. When tests rerun, the placeholders (<code>{{jwt_token}}</code>, <code>{{session_id}}</code>, etc.) are filled dynamically, ensuring a seamless, no-fail execution.</p>
<p>This approach also helps if you need to <a href="https://keploy.io/blogs/comparing-json-objects/">compare JSON</a> before and after reruns to validate that only the expected fields changed.</p>
<h2>Benefits of Recursion in JSON Templatization</h2>
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<p><strong>Flexibility</strong>: Handles JSON structures of any complexity or depth.</p>
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<p><strong>Consistency</strong>: Dynamic fields update automatically without breaking tests.</p>
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<p><strong>Efficiency</strong>: Reduces manual intervention and missed updates.</p>
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<h2>Challenges and Considerations</h2>
<p>While recursion solves nested JSON problems elegantly, it also risks <strong>stack overflow</strong> for very large inputs. At Keploy, we carefully optimized our recursive function to ensure high performance even with deeply nested or large JSON payloads.</p>
<p>Code readability is another factor. Recursive solutions must be clean, well-documented, and easy to maintain—a principle we prioritized throughout Keploy’s templatization implementation.</p>
<h2>Conclusion</h2>
<p>Adding templates to JSON is far from trivial, but recursion offers a powerful and elegant solution.</p>
<p>At <a href="https://keploy.io/">Keploy</a>, we successfully leveraged recursion to traverse and templatize dynamic fields inside complex JSON structures, greatly improving the reliability of our <a href="https://keploy.io/docs/introduction">test automation</a> features.</p>
<p>In short, recursion helped Keploy automate the challenge of dynamic fields, ensuring seamless, up-to-date, and robust test case management.</p>
<h2>FAQ’S</h2>
<h3><strong>1. What is JSON templatization?</strong></h3>
<p>JSON templatization is the process of replacing dynamic fields like tokens and timestamps with placeholders inside JSON data. This helps in keeping test cases stable even when data changes between requests. It’s essential for handling dynamic APIs during automated testing. Keploy uses recursion to templatize complex JSON structures effectively.</p>
<h3><strong>2. Why is recursion important for JSON templatization?</strong></h3>
<p>Recursion helps traverse deeply nested JSON objects without knowing their depth beforehand. It ensures that dynamic fields at any level are identified and replaced accurately. This approach keeps the structure intact while automating updates. Keploy leverages recursion to build a flexible and robust templatization engine.</p>
<h3><strong>3. How does Keploy identify fields for JSON templatization?</strong></h3>
<p>Keploy automatically detects “noisy” fields like JWT tokens, timestamps, and session data. These fields typically change between requests and could cause test failures. By templatizing them, tests stay reliable even with dynamic data. This is part of Keploy’s smarter approach to automated testing.</p>
<h3><strong>4. What are the challenges of using recursion for JSON handling?</strong></h3>
<p>Handling very large or deeply nested JSON can risk stack overflow if recursion isn’t optimized. Also, recursive code can become complex and harder to maintain over time. At Keploy, special care is taken to keep recursion efficient and readable. This ensures performance while scaling to complex test cases.</p>

         
        
                    

                    
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Posts by Pranshu Srivastava

Understanding JSON Templatization with Recursion for Dynamic Data Handling

eBPF for TLS Traffic Tracing: Secure & Efficient Observability

Tracing TLS Data with Ethical and Secure Practices

Efficient TCP Server Connection Management

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