{"id":18008,"date":"2026-05-05T16:00:37","date_gmt":"2026-05-05T16:00:37","guid":{"rendered":"https:\/\/testgrid.io\/blog\/?p=18008"},"modified":"2026-05-05T16:00:39","modified_gmt":"2026-05-05T16:00:39","slug":"predictive-test-selection","status":"publish","type":"post","link":"https:\/\/testgrid.io\/blog\/predictive-test-selection\/","title":{"rendered":"Predictive Test Selection: How It Works, Use Cases, and Best Practices"},"content":{"rendered":"\n<p>A recent <a href=\"https:\/\/www.tricentis.com\/news\/tricentis-releases-inaugural-quality-transformation-report\" target=\"_blank\" rel=\"noopener\">report<\/a> shows that about 45% of development, quality assurance, and DevOps teams focus on enhancing delivery speed rather than prioritizing software quality. And nearly 42% of global organizations feel that poor software quality is costing them over $1million annually.<\/p>\n\n\n\n<p>These numbers aren\u2019t surprising given the fact that teams today are under constant pressure to expedite release cycles.<\/p>\n\n\n\n<p>Each time you add a new feature, you have to run your entire test suite to ensure all the existing workflows are intact and that you don\u2019t miss any critical defects. This isn\u2019t really sustainable when your developers deploy multiple times a day.<\/p>\n\n\n\n<p>What you need is a smart way of identifying and executing only those tests which got affected by the recent changes. Predictive test selection helps you do that.<\/p>\n\n\n\n<p>In this blog, we\u2019ll see what predictive test selection is, the tech behind it, how it works, and the best practices you should follow.<\/p>\n\n\n\n<p>See how CoTester predicts and runs the tests that actually matter. <a href=\"https:\/\/public.testgrid.io\/signup?form=cotester-starter-package\">Request a free trial<\/a>.<\/p>\n\n\n\n<section class=\"wp-block-custom-tldr-summary tldr-block\"><p class=\"tldr-label\">TL;DR<\/p><ul class=\"tldr-list\"><li><span>Manual test prioritization relies on static rules and human judgment, which can be inconsistent and hard to scale<\/span><\/li><li><span>Predictive test selection is an AI-driven test optimization method that leverages AI models trained on historical results to prioritize tests based on their predicted likelihood of failure<\/span><\/li><li><span>Predictive test selection process includes steps like historical data collection, model training, predictive modeling, test execution, feedback, and continuous improvement<\/span><\/li><li><span>You can apply predictive selection in unit, integration, regression, performance, and security testing<\/span><\/li><li><span>This can help you reduce overall testing time, save compute resources, accelerate defect detection, and minimize redundant executions<\/span><\/li><\/ul><\/section>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>What Is Predictive Test Selection?<\/strong><\/h2>\n\n\n\n<p>Predictive test selection is basically an application of predictive analytics where AI and machine learning models study patterns from historical signals like change impact, test outcomes, and <a href=\"https:\/\/testgrid.io\/blog\/guide-to-write-an-effective-bug-report\/\">bug reports<\/a> to identify the tests that are highly likely to uncover defects.<\/p>\n\n\n\n<p>These models are adaptive, which means they learn from every test execution and feedback, perform test case prioritization using machine learning, improve their prediction accuracy, and ensure your testing process stays relevant with your changing app behavior.<\/p>\n\n\n\n<p><strong>Also Read<\/strong>: <a href=\"https:\/\/testgrid.io\/blog\/ai-testing\/\">AI Testing: What It Is, What It Isn\u2019t, and Why It Matters<\/a><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Predictive Test Selection vs Manual Test Prioritization<\/h2>\n\n\n\n<p>Traditional testing depends on predefined rules, based on which you run most, or rather, all tests with equal importance. Testers decide what tests to execute according to their past experiences with issues, knowledge of the codebase, and perceived risk of certain app features.<\/p>\n\n\n\n<p>This can create inconsistency because different testers might prioritize tests differently. Plus, this manual process can be extremely hard to maintain when your test suites expand.<\/p>\n\n\n\n<p>Here is a more detailed difference between manual prioritization and predictive test selection:<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th class=\"has-text-align-left\" data-align=\"left\">Aspect<\/th><th class=\"has-text-align-left\" data-align=\"left\">Manual Prioritization<\/th><th class=\"has-text-align-left\" data-align=\"left\">Predictive Test Selection<\/th><\/tr><\/thead><tbody><tr><td class=\"has-text-align-left\" data-align=\"left\">Decision basis<\/td><td class=\"has-text-align-left\" data-align=\"left\">Relies on tester experience, intuition, and heuristics<\/td><td class=\"has-text-align-left\" data-align=\"left\">Uses data-driven models trained on historical test results and code changes<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\">Consistency<\/td><td class=\"has-text-align-left\" data-align=\"left\">Varies across team members, leading to inconsistent outcomes<\/td><td class=\"has-text-align-left\" data-align=\"left\">Produces consistent results by removing subjectivity in decision-making<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\">Adaptability<\/td><td class=\"has-text-align-left\" data-align=\"left\">Static and does not respond well to real-time code changes<\/td><td class=\"has-text-align-left\" data-align=\"left\">Dynamically adapts to new commits and evolving codebases<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\">Scalability<\/td><td class=\"has-text-align-left\" data-align=\"left\">Difficult to manage as test suites and systems grow<\/td><td class=\"has-text-align-left\" data-align=\"left\">Scales efficiently with large and complex test suites<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\">Test selection approach<\/td><td class=\"has-text-align-left\" data-align=\"left\">Typically ranks tests, but still depends on human judgment<\/td><td class=\"has-text-align-left\" data-align=\"left\">Selects and prioritizes tests based on predicted failure likelihood<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\">Efficiency<\/td><td class=\"has-text-align-left\" data-align=\"left\">Time-consuming and prone to running unnecessary tests<\/td><td class=\"has-text-align-left\" data-align=\"left\">Focuses on high-risk areas, reducing redundant executions<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><strong><strong>How Does Predictive Test Selection Work? The Stepwise Flow<\/strong><\/strong><\/h2>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1024\" height=\"219\" src=\"https:\/\/testgrid.io\/blog\/wp-content\/uploads\/2026\/05\/how-predictive-test-selection-works-1024x219.webp\" alt=\"How Predictive Test Selection Works\" class=\"wp-image-18024\" loading=\"lazy\" title=\"\" srcset=\"https:\/\/testgrid.io\/blog\/wp-content\/uploads\/2026\/05\/how-predictive-test-selection-works-1024x219.webp 1024w, https:\/\/testgrid.io\/blog\/wp-content\/uploads\/2026\/05\/how-predictive-test-selection-works-300x64.webp 300w, https:\/\/testgrid.io\/blog\/wp-content\/uploads\/2026\/05\/how-predictive-test-selection-works-768x164.webp 768w, https:\/\/testgrid.io\/blog\/wp-content\/uploads\/2026\/05\/how-predictive-test-selection-works-1536x328.webp 1536w, https:\/\/testgrid.io\/blog\/wp-content\/uploads\/2026\/05\/how-predictive-test-selection-works-150x32.webp 150w, https:\/\/testgrid.io\/blog\/wp-content\/uploads\/2026\/05\/how-predictive-test-selection-works.webp 1919w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p><strong>1. Collecting historical data<\/strong>: Historical data analysis is the primary way your AI system selects critical tests.<\/p>\n\n\n\n<p>This means the AI models connect with your <a href=\"https:\/\/testgrid.io\/blog\/ci-cd-tools\/\">continuous integration tools<\/a>, test runners, and version control systems to gather historical data such as previous test results, defect patterns, and test coverage, and then identify which tests can potentially fail and which ones can catch severe defects.<\/p>\n\n\n\n<p>Machine learning models also assess metadata, such as which parts of your code were modified and the impact of that on tests, for making predictions.<\/p>\n\n\n\n<p><strong>2. Model training<\/strong>: After the necessary data is in place, you\u2019ll need to train the ML models to spot the patterns between the code changes and test outcomes.<\/p>\n\n\n\n<p>Most <a href=\"https:\/\/testgrid.io\/blog\/ai-testing-tools\/\">AI testing tools<\/a> or systems today depend on supervised learning, where the model learns from labeled data, like passed or failed tests, to know which tests to execute first.<\/p>\n\n\n\n<p>You feed information such as changed files and failure frequency into the model, and over time, it can predict which of your test cases have a high probability of failing.<\/p>\n\n\n\n<p><strong>Also Read:<\/strong> <a href=\"https:\/\/testgrid.io\/blog\/ai-model-testing\/\">AI Model Testing: Methods, Challenges, and How to Test AI Models<\/a><\/p>\n\n\n\n<p><strong>3. Predictive modeling<\/strong>: After you train your AI model, it will use your inputs, like code modifications and test dependencies, and assign a failure probability score to your tests. Now, as per this score, your AI system will rank your tests by their value (in catching defects) and risk.<\/p>\n\n\n\n<p><strong>4. Intelligent test execution<\/strong>: Now, as per the failure probability score, the tests that have the highest score will be run on priority. Your AI system will execute these tests across <a href=\"https:\/\/testgrid.io\/blog\/test-environment\/\">environments<\/a> so you can find the critical defects early in your <a href=\"https:\/\/testgrid.io\/blog\/software-development-life-cycle\/\">development cycle<\/a>.<\/p>\n\n\n\n<p>This intelligent test execution approach helps you reduce the number of tests without affecting test coverage.<\/p>\n\n\n\n<p><strong>5. Continuous feedback and improvement<\/strong>: The result of every test run (i.e., the pass and fail data) is fed back into your test pipeline, so your AI system gets continuous insights for retraining and updating itself with fresh information.<\/p>\n\n\n\n<p>This step is very important because it allows your model to adapt to new features, changing codebases, and defect trends, and predict better with time.<\/p>\n\n\n\n<p><strong>Read More<\/strong>: <a href=\"https:\/\/testgrid.io\/blog\/how-ai-changes-software-delivery\/\">How AI Is Changing Software Delivery<\/a><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Where Predictive Test Selection Really Makes a Difference<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>1. Unit testing<\/strong><\/h3>\n\n\n\n<p><a href=\"https:\/\/testgrid.io\/blog\/ai-unit-testing\/\">Unit tests<\/a> help you test individual components like classes, modules, functions, and isolation methods.<\/p>\n\n\n\n<p>So, whenever you add changes to your code, the AI model can identify and map these changes to the affected unit tests (by analyzing dependency graphs and code coverage data), which helps you avoid executing irrelevant or redundant tests.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2. Integration testing<\/strong><\/h3>\n\n\n\n<p><a href=\"https:\/\/testgrid.io\/blog\/integration-testing-types-approaches\/\">Integration tests<\/a> can take a long time because you check how the different components and service integrations of your app work together, which is why predictive test selection can be very helpful here.<\/p>\n\n\n\n<p>Rather than running your full integration suite, the AI system highlights API contracts, shared modules, or service dependencies that were impacted by the code changes.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>3. Regression testing<\/strong><\/h3>\n\n\n\n<p>You\u2019ll actually realize how important predictive test selection is when your test suite grows as you add more features and updates to your app.<\/p>\n\n\n\n<p>Traditional regression tests often run for long hours or overnight. And even though many teams already automate the <a href=\"https:\/\/testgrid.io\/blog\/regression-testing\/\" data-type=\"link\" data-id=\"https:\/\/testgrid.io\/blog\/regression-testing\/\">process of regression testing<\/a>, running all the tests after every change can require significant time and resources.<\/p>\n\n\n\n<p>With predictive selection, you can focus on the tests that cover the high-risk features of your app and have higher chances of failure.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>4. Performance testing<\/strong><\/h3>\n\n\n\n<p>For simulating real-world usage conditions and load scenarios for <a href=\"https:\/\/testgrid.io\/blog\/performance-testing-guide\/\" data-type=\"link\" data-id=\"https:\/\/testgrid.io\/blog\/performance-testing-guide\/\">performance testing<\/a>, you need time and infrastructure, which can be expensive.<\/p>\n\n\n\n<p>Predictive test selection allows you to recognize the modifications in critical code paths or database queries that can potentially affect your app\u2019s performance, and highlight only the most relevant tests.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>5. Security testing<\/strong><\/h3>\n\n\n\n<p>Predictive test selection helps you make your <a href=\"https:\/\/testgrid.io\/blog\/security-testing\/\">security testing<\/a> process a lot more targeted. So, after your AI model analyzes the code updates, it maps them to the affected APIs, authentication mechanisms, and data flows, and then emphasizes the tests linked to those areas.<\/p>\n\n\n\n<p>ML models can also study past vulnerabilities, such as injection flaws or misconfigurations, and allow you to run the tests that cover critical workflows that handle customer PII.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Why Is Predictive Test Selection Important?<\/strong><\/h2>\n\n\n\n<p>Most enterprise-level development and QA teams today have thousands of <a href=\"https:\/\/testgrid.io\/blog\/how-to-write-test-cases\/\" data-type=\"link\" data-id=\"https:\/\/testgrid.io\/blog\/how-to-write-test-cases\/\">test cases<\/a> in their test repositories. The more upgrades you add, the more tests you need to run. Executing the entire suite after even minor code modifications isn\u2019t really practical.<\/p>\n\n\n\n<p>What if you are running the tests that didn\u2019t even get impacted by the changes? This is exactly why predictive test selection in software testing is important.<\/p>\n\n\n\n<p>It helps you:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Prevent pipeline slowdowns caused by increasing test suites<\/li>\n\n\n\n<li>Reduce the delays that can hinder developer productivity and release timelines<\/li>\n\n\n\n<li>Maintain the quality of your product or app without increasing testing overhead<\/li>\n\n\n\n<li>Eliminate manual change impact analysis and focus more on the failure-prone areas<\/li>\n\n\n\n<li>Keep your test suite and processes lean and maintainable when your app scales<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Challenges in Predictive Test Selection (and How to Overcome Them<\/strong>)<\/h2>\n\n\n\n<p><strong>1. Data availability and quality<\/strong>: Since your AI models depend largely on historical data for test selection, inconsistent or incomplete data, such as missing test results, flaky tests, and redundant logs across tools, can result in inaccurate predictions.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-background has-fixed-layout\" style=\"background-color:#fafafa\"><tbody><tr><td><strong>Best practice<\/strong><br>You need to set up a unified data pipeline that integrates with your version control systems, execution engines, and <a href=\"https:\/\/testgrid.io\/blog\/ci-cd-test-automation\/\">CI\/CD pipelines<\/a> so that your model always has access to relevant and updated data. You can also <a href=\"https:\/\/testgrid.io\/blog\/testing-documentation\/\">standardize logging formats<\/a> and ensure traceability between code commits and tests to improve accuracy.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p><strong>2. Model training and maintenance<\/strong>: When training AI models, you&#8217;re working with high-dimensional data like code diff, test mappings, and component relationships. This data changes constantly. So if you don\u2019t properly retrain, feature engineer, and monitor your models, it may lead to model drift, overfitting, or outdated predictions.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-background has-fixed-layout\" style=\"background-color:#fafafa\"><tbody><tr><td><strong>Best practice<\/strong><br>You can establish automated retraining pipelines so that your models are continuously trained with fresh data. Other than this, use incremental learning for evolving codebases and regularly monitor your model\u2019s performance with metrics like precision, recall, and F1-score.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p><strong>Also Read<\/strong>: <a href=\"https:\/\/testgrid.io\/blog\/what-is-agentic-ai\/\">Inside Agentic AI: How Machines Learn to Perceive, Reason, and Act<\/a><\/p>\n\n\n\n<p><strong>3. Integration complexity<\/strong>: Your AI systems must seamlessly integrate with version control, CI\/CD, test management, reporting, and code coverage tools to be able to correctly perform predictive test selection. But if test names, file paths, and identifiers don&#8217;t match across these tools, your model cannot link changes to the right tests.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-background has-fixed-layout\" style=\"background-color:#fafafa\"><tbody><tr><td><strong>Best practice<\/strong><br>You should first build a strong integration layer via APIs, webhooks, and plugins, which will help your AI system to connect with your <a href=\"https:\/\/testgrid.io\/blog\/software-testing-tools\/\">testing tools<\/a>. Then, standardize the data format and maintain uniform naming conventions including commit and test IDs.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p><strong>4. Balancing between predictive selection and comprehensive testing<\/strong>: AI models may miss covering some of the critical scenarios, particularly edge cases or newly added features, because of limited historical data available. And this can reduce your test coverage and increase <a href=\"https:\/\/testgrid.io\/blog\/testing-in-production\/\">production failures<\/a>. So, assessing when you need predictive test selection and when to perform comprehensive <a href=\"https:\/\/testgrid.io\/blog\/end-to-end-testing-a-detailed-guide\/\">end-to-end testing<\/a> can be tricky.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Best practice<\/strong><br>The best strategy is to go for a hybrid approach. Combine AI-based test selection with periodic full suite tests, and use a confidence threshold to know when you should expand test coverage.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Implement Predictive Test Selection with CoTester<\/h2>\n\n\n\n<p>For efficient test selection, you need a system that understands your application context, keeps tests aligned with real workflows, and adapts as your application changes.<\/p>\n\n\n\n<p><a href=\"https:\/\/testgrid.io\/cotester\">CoTester<\/a> is an AI testing agent that learns from your product artifacts, including user stories, requirement documents, and existing test assets. It converts these inputs into structured test cases that reflect how your application behaves in production.<\/p>\n\n\n\n<p>As changes are introduced across workflows, configurations, or releases, CoTester keeps test coverage aligned with the updated behavior. Instead of relying on static test suites, you work with tests that stay connected to the current application state.<\/p>\n\n\n\n<p>You can review, refine, and approve test steps before execution, ensuring validation remains consistent with intended behavior.<\/p>\n\n\n\n<p>CoTester integrates with your delivery pipeline to trigger test runs at the right points, whether during releases, scheduled cycles, or validation checkpoints. This keeps execution focused and avoids unnecessary runs.<\/p>\n\n\n\n<p>During execution, CoTester captures logs, screenshots, and step-level outcomes. Each result stays linked to its originating requirement, giving you clear visibility into what was validated and where gaps may exist.<\/p>\n\n\n\n<p>To explore how CoTester fits into your testing workflow, <a href=\"https:\/\/public.testgrid.io\/signup?form=cotester-starter-package\">request a trial<\/a>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Frequently Asked Questions (FAQs)<\/strong><\/h2>\n\n\n<div id=\"rank-math-faq\" class=\"rank-math-block\">\n<div class=\"rank-math-list \">\n<div id=\"faq-question-1777903570002\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>What are the best predictive test selection tools available?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Some of the top tools for predictive test selection in 2026 are CoTester, mabl, Testim, Functionize, and Tricentis Tosca. These tools leverage machine learning and have features like risk-based test prioritization, self-healing automation, and predictive defect detection to help you run the most critical tests.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1777903581673\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>What\u2019s the difference in predictive test selection vs test case prioritization?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Predictive test selection helps you narrow down the tests based on recent code changes and historical failure patterns. Test case prioritization basically ranks those tests based on risk and impact, and allows you to decide in which order you should run the tests.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1777903590486\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>How to reduce test execution time in CI\/CD with predictive test selection?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>You can cut down your execution time by implementing predictive test selection alongside test orchestration. This will allow you to dynamically allocate compute resources to high-risk tests, execute them in parallel across environments, skip redundant test runs, and accelerate defect discovery.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1777903604961\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>How does predictive test selection support shift-left testing?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Incorporating predictive test selection into your CI\/CD workflow enables your team to trigger the affected tests immediately after code commits rather than waiting for the development process to complete. Your developers get signs of potential failures early and fix the issues before they become too complex.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1777903617299\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>Can predictive test selection adapt to rapidly changing codebases?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Most modern AI systems are designed to adapt to continuously evolving codebases. They are trained regularly on new execution data so that they can keep up with frequent app updates and architectural changes, and recalibrate predictions based on failure patterns without compromising accuracy.<\/p>\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>A recent report shows that about 45% of development, quality assurance, and DevOps teams focus on enhancing delivery speed rather than prioritizing software quality. And nearly 42% of global organizations feel that poor software quality is costing them over $1million annually. These numbers aren\u2019t surprising given the fact that teams today are under constant pressure [&hellip;]<\/p>\n","protected":false},"author":26,"featured_media":18022,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[772],"tags":[],"class_list":["post-18008","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-software-testing-guide"],"acf":[],"images":{"medium":"https:\/\/testgrid.io\/blog\/wp-content\/uploads\/2026\/05\/Predictive-Test-Selection-300x169.webp","large":"https:\/\/testgrid.io\/blog\/wp-content\/uploads\/2026\/05\/Predictive-Test-Selection-1024x576.webp"},"_links":{"self":[{"href":"https:\/\/testgrid.io\/blog\/wp-json\/wp\/v2\/posts\/18008","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/testgrid.io\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/testgrid.io\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/testgrid.io\/blog\/wp-json\/wp\/v2\/users\/26"}],"replies":[{"embeddable":true,"href":"https:\/\/testgrid.io\/blog\/wp-json\/wp\/v2\/comments?post=18008"}],"version-history":[{"count":9,"href":"https:\/\/testgrid.io\/blog\/wp-json\/wp\/v2\/posts\/18008\/revisions"}],"predecessor-version":[{"id":18028,"href":"https:\/\/testgrid.io\/blog\/wp-json\/wp\/v2\/posts\/18008\/revisions\/18028"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/testgrid.io\/blog\/wp-json\/wp\/v2\/media\/18022"}],"wp:attachment":[{"href":"https:\/\/testgrid.io\/blog\/wp-json\/wp\/v2\/media?parent=18008"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/testgrid.io\/blog\/wp-json\/wp\/v2\/categories?post=18008"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/testgrid.io\/blog\/wp-json\/wp\/v2\/tags?post=18008"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}