{"id":24530,"date":"2026-10-13T11:15:30","date_gmt":"2026-10-13T11:15:30","guid":{"rendered":"https:\/\/engineerbabu.com\/blog\/?p=24530"},"modified":"2026-10-13T11:15:30","modified_gmt":"2026-10-13T11:15:30","slug":"a-b-testing-in-mobile-apps","status":"publish","type":"post","link":"https:\/\/engineerbabu.com\/blog\/a-b-testing-in-mobile-apps\/","title":{"rendered":"A\/B Testing in Mobile Apps: Tools, Process and Pitfalls"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">Your last release shipped a redesigned paywall. Revenue moved 4% that week. Was it the paywall, a seasonal dip, or the iOS update that landed the same morning?<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Without a control group, nobody in that meeting can answer honestly. They can only argue confidently.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">That gap between a confident answer and a correct one is the entire reason A\/B testing in mobile apps exists.<\/span><\/p>\n<h2><b>TL;DR<\/b><\/h2>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">A\/B testing in mobile apps splits live users across variants, so release decisions rest on evidence instead of on whoever argues hardest.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Mobile adds constraints web never had: store review delays, SDK caching, users stranded on old builds, and thinner daily samples.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Firebase, Statsig, Amplitude Experiment, Optimizely and LaunchDarkly cover most needs, and the right pick depends on your traffic and how much statistics you want to own.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">EngineerBabu wires experimentation and feature flags into the release pipeline early, so teams can test changes instead of defending them.<\/span><\/li>\n<\/ul>\n<h2><b>What A\/B testing in mobile apps actually is<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">A\/B testing in mobile apps means randomly assigning live users to two or more versions of a screen, flow, or price, then comparing a single pre-chosen metric between them. Everything else stays constant. The randomization is what lets you claim causation rather than coincidence.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">It is not the same as QA.<\/span><a href=\"https:\/\/engineerbabu.com\/blog\/what-is-software-testing\/\"> <span style=\"font-weight: 400;\">Software testing<\/span><\/a><span style=\"font-weight: 400;\"> asks whether the build works. Experimentation asks whether the build is better. You need both, and they answer different questions at different points in the<\/span><a href=\"https:\/\/engineerbabu.com\/blog\/mobile-app-development-cycle-the-ideal-method\/\"> <span style=\"font-weight: 400;\">mobile app development cycle<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The payoff is well documented. Mobile-only experiments win 21.4% of the time versus 14.7% for desktop-only tests, and winning mobile tests deliver larger average lifts, according to Visionary&#8217;s 2026 study of more than 4,200 experiments (<\/span><a href=\"https:\/\/visionary-marketing.co.uk\/blog\/ab-testing-statistics-2026\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">source<\/span><\/a><span style=\"font-weight: 400;\">). Mobile interfaces start rougher, so there is simply more room to improve.<\/span><\/p>\n<h2><b>Why mobile experimentation is harder than web<\/b><\/h2>\n<ul>\n<li aria-level=\"1\">\n<h3><b>You cannot hotfix a losing variant<\/b><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">On the web, a bad variant dies the second you flip it off. In an app, the variant is compiled into a binary sitting on a user&#8217;s phone. If you hardcode the experiment, killing it means a new build, a store review, and a slow trickle of updates.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This is why variants belong behind remote config or feature flags, never behind an app version. Flag-controlled tests turn off instantly. Version-controlled tests do not.<\/span><\/p>\n<ul>\n<li aria-level=\"1\">\n<h3><b>Your sample is smaller than the dashboard suggests<\/b><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Monthly active users look reassuring. Daily users entering the specific screen you are testing look much less so.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">A checkout experiment only counts users who reach checkout. Split that across two variants and your real sample can be a fraction of what you assumed. Most teams need roughly 1,000 to 2,000 users per variant to spot a 5% effect, and several thousand more for smaller ones.<\/span><\/p>\n<ul>\n<li aria-level=\"1\">\n<h3><b>Version fragmentation pollutes results<\/b><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Users on older builds never see your variant but still show up in platform analytics. If your exposure tracking is sloppy, those users contaminate the control group and flatten any real difference. Always segment by the minimum app version that contains the experiment.<\/span><\/p>\n<h2><b>Tools for A\/B testing in mobile apps<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">Server-side experimentation has gone mainstream, with 47.4% of programs running server-side tests in 2026, up from 18.4% in 2022. That shift matters for apps, because server-side assignment sidesteps store review entirely.<\/span><\/p>\n<table>\n<tbody>\n<tr>\n<td><b>Tool<\/b><\/td>\n<td><b>Pricing model<\/b><\/td>\n<td><b>Standout strength<\/b><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Firebase A\/B Testing<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Free with Firebase<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Fastest path to a first test<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Statsig<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Usage based, generous free tier<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Warehouse-native stats engine<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Amplitude Experiment<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Add-on to Amplitude<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Analytics and experiments in one place<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Optimizely Feature Experimentation<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Enterprise contract<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Governance across large orgs<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">LaunchDarkly<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Per seat plus usage<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Flag reliability at scale<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<ul>\n<li aria-level=\"1\">\n<h3><b>Firebase A\/B Testing<\/b><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Firebase pairs Remote Config with Google Analytics so you can swap values, copy, and layouts without a release. It is the default starting point for most teams because it costs nothing and the SDK is already in the project.<\/span><\/p>\n<p><b>Key features:<\/b><span style=\"font-weight: 400;\"> Remote Config targeting, Bayesian result reporting, native iOS and Android SDKs, integration with Crashlytics and push campaigns.<\/span><\/p>\n<p><b>Best for:<\/b><span style=\"font-weight: 400;\"> Early-stage teams running their first handful of experiments on a modest budget.<\/span><\/p>\n<ul>\n<li aria-level=\"1\">\n<h3><b>Statsig<\/b><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Statsig runs experiments against your own data warehouse, which means the metrics in the experiment match the metrics in your board deck. Sequential testing support helps teams that struggle to leave tests alone.<\/span><\/p>\n<p><b>Key features:<\/b><span style=\"font-weight: 400;\"> Warehouse-native analysis, sequential and CUPED methods, feature gates, automated metric guardrails, holdout groups.<\/span><\/p>\n<p><b>Best for:<\/b><span style=\"font-weight: 400;\"> Product teams with a data engineer and a real appetite for statistical rigor.<\/span><\/p>\n<ul>\n<li aria-level=\"1\">\n<h3><b>Amplitude Experiment<\/b><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">If your product analytics already live in Amplitude, running experiments there removes an entire integration layer. Exposure events and behavioral cohorts share one definition, which kills a common source of disagreement.<\/span><\/p>\n<p><b>Key features:<\/b><span style=\"font-weight: 400;\"> Cohort-based targeting, shared metric definitions, remote evaluation, funnel-level impact analysis.<\/span><\/p>\n<p><b>Best for:<\/b><span style=\"font-weight: 400;\"> Teams already invested in Amplitude who want experiments tied to existing funnels.<\/span><\/p>\n<ul>\n<li aria-level=\"1\">\n<h3><b>Optimizely Feature Experimentation<\/b><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Optimizely is built for organizations where many squads ship into one app and someone has to prevent collisions. Governance, approvals, and audit trails are the real product here.<\/span><\/p>\n<p><b>Key features:<\/b><span style=\"font-weight: 400;\"> Multi-team permissions, stats engine with always-valid results, mutual exclusion groups, broad SDK coverage.<\/span><\/p>\n<p><b>Best for:<\/b><span style=\"font-weight: 400;\"> Enterprises running dozens of concurrent experiments across multiple products.<\/span><\/p>\n<ul>\n<li aria-level=\"1\">\n<h3><b>LaunchDarkly<\/b><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">LaunchDarkly started as flag infrastructure and added experimentation on top. Choose it when safe rollout matters more than deep analysis, such as staged releases and instant kill switches.<\/span><\/p>\n<p><b>Key features:<\/b><span style=\"font-weight: 400;\"> Progressive delivery, kill switches, approval workflows, experimentation layered on existing flags.<\/span><\/p>\n<p><b>Best for:<\/b><span style=\"font-weight: 400;\"> Engineering-led teams who treat flags as release infrastructure first and experiments second.<\/span><\/p>\n<h2><b>The process, step by step<\/b><\/h2>\n<h3><b>Step 1: Pick one metric before anything else<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Decide the single number this experiment is allowed to move. Trial starts, day-7 retention, checkout completion, pick one. Write it down before you look at any data.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Secondary metrics exist to catch damage, not to rescue a failed test. If your primary metric flatlines and you declare victory on a secondary one, you have not run an experiment. You have run a search for good news.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Add two guardrail metrics as well, usually crash-free sessions and app launch time. A variant that lifts conversion while slowing cold start is not a win.<\/span><\/p>\n<h3><b>Step 2: Write a falsifiable hypothesis<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">A usable hypothesis names the change, the audience, the expected direction, and the reason. &#8220;If we drop phone verification from signup for first-time Android users, completion should rise roughly 10%, because session replays show heavy drop-off on that screen.&#8221;<\/span><\/p>\n<p><span style=\"font-weight: 400;\">That sentence forces you to state a size, which forces a sample size calculation. Vague hypotheses like &#8220;improve onboarding&#8221; produce tests nobody can call.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Ground the reasoning in observed behavior from analytics or replays. Experiments built on evidence win far more often than experiments built on a stakeholder&#8217;s instinct.<\/span><\/p>\n<h3><b>Step 3: Calculate sample size and duration up front<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Plug your baseline conversion rate, your minimum detectable effect, and your daily eligible users into a sample size calculator. The output tells you how long the test must run, and sometimes it tells you not to run it at all.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">High-traffic apps can read a 5% effect within a week. Mid-sized apps usually need two to three weeks. Below roughly 50,000 daily users, only large effects are detectable in a reasonable window.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Always run in full weekly cycles. Weekday and weekend behavior differ enough to swing a short test.<\/span><\/p>\n<h3><b>Step 4: Ship both variants behind a flag<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Build control and variant into the same release, gated remotely. Verify assignment in a staging environment, confirm exposure events fire exactly once per user, and check that a user never flips between variants across sessions.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Then roll out slowly. Start at 5% of eligible users, watch crash rates and latency for a day, then open to the planned split. This staged approach is standard practice in<\/span><a href=\"https:\/\/engineerbabu.com\/blog\/strategies-for-mobile-app-performance-optimization\/\"> <span style=\"font-weight: 400;\">mobile app performance optimization<\/span><\/a><span style=\"font-weight: 400;\"> and it prevents a bad variant from reaching your whole base.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Document the flag owner and the planned cleanup date in the same ticket.<\/span><\/p>\n<h3><b>Step 5: Leave it alone while it runs<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Resist daily significance checks. Early results swing wildly, and stopping the moment a variant looks ahead inflates false positives badly. If your platform supports sequential testing, use it, because it is built for continuous monitoring.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">What you should watch daily is damage: crash rate, ANRs, API error rate, and session length collapse. Those justify an immediate stop.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Everything else waits for the predetermined end date. The discipline is boring, and it is the difference between an experiment and a guess.<\/span><\/p>\n<h3><b>Step 6: Decide, roll out, and remove the flag<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Read the result against the metric you declared in step one. Three outcomes are valid: ship it, kill it, or ship it because it is neutral but cheaper to maintain.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Flat results are information, not failure. Roughly four out of five tests do not produce a winner, which is exactly why the winners pay for the program.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Then remove the dead code and retire the flag. Stale flags accumulate into a maintenance tax that shows up later during<\/span><a href=\"https:\/\/engineerbabu.com\/blog\/mobile-app-maintenance-and-support\/\"> <span style=\"font-weight: 400;\">mobile app maintenance and support<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h2><b>Pitfalls that quietly ruin results<\/b><\/h2>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Peeking and early stopping.<\/b><span style=\"font-weight: 400;\"> Checking results daily and stopping at the first green number is the most common way teams fool themselves.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Changing five things at once.<\/b><span style=\"font-weight: 400;\"> A redesigned screen with new copy, new colors, and a new CTA tells you nothing about which element moved the number.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Ignoring novelty effects.<\/b><span style=\"font-weight: 400;\"> Returning users react to anything new. Give the test two full weeks before trusting engagement lifts.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Mismatched exposure tracking.<\/b><span style=\"font-weight: 400;\"> If the control group includes users who never loaded the experiment, every effect gets diluted toward zero.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Testing with too little traffic.<\/b><span style=\"font-weight: 400;\"> Running an underpowered test is worse than running none, because it produces a number people then quote.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Skipping guardrails.<\/b><span style=\"font-weight: 400;\"> Treating crash rate and latency as optional is one of the more expensive<\/span><a href=\"https:\/\/engineerbabu.com\/blog\/mobile-app-development-mistakes\/\"> <span style=\"font-weight: 400;\">mobile app development mistakes<\/span><\/a><span style=\"font-weight: 400;\"> teams repeat.<\/span><\/li>\n<\/ul>\n<h2><b>What to test first<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">Start where money and drop-off concentrate, not where opinions are loudest.<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Paywall and pricing.<\/b><span style=\"font-weight: 400;\"> Trial length, price anchoring, and layout usually produce the largest revenue swings.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Onboarding length.<\/b><span style=\"font-weight: 400;\"> Screen count and permission timing directly affect activation.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Push notification copy and timing.<\/b><span style=\"font-weight: 400;\"> Cheap to test, immediate feedback.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Empty states and first-session defaults.<\/b><span style=\"font-weight: 400;\"> These shape whether<\/span><a href=\"https:\/\/engineerbabu.com\/blog\/features-of-a-successful-mobile-app\/\"> <span style=\"font-weight: 400;\">features of a successful mobile app<\/span><\/a><span style=\"font-weight: 400;\"> get discovered at all.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Store listing assets.<\/b><span style=\"font-weight: 400;\"> Play Console experiments test icons and screenshots before users ever install.<\/span><\/li>\n<\/ol>\n<h2><b>When A\/B testing in mobile apps is the wrong tool<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">Low traffic is the clearest case. If you cannot reach a few thousand users per variant within a month, run usability sessions and session replays instead. Qualitative research explains why people struggle, which quantitative tests never do.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Pre-launch products are another. During<\/span><a href=\"https:\/\/engineerbabu.com\/blog\/mvp-vs-full-scale-development\/\"> <span style=\"font-weight: 400;\">MVP vs full scale development<\/span><\/a><span style=\"font-weight: 400;\"> decisions, you are testing whether anyone wants the product, not which button color wins.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Personalization is a third. When behavior varies sharply by segment,<\/span><a href=\"https:\/\/engineerbabu.com\/blog\/predictive-analytics-in-mobile-apps\/\"> <span style=\"font-weight: 400;\">predictive analytics in mobile apps<\/span><\/a><span style=\"font-weight: 400;\"> often beats a single global winner, because the best variant differs by user.<\/span><\/p>\n<h2><b>Final thoughts<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">A\/B testing in mobile apps is less about tooling than about discipline. Teams fail at it because they peek, they test five things at once, and they move the goalposts after seeing results.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The teams that win pick one metric, size the test honestly, leave it alone, and accept flat outcomes without spin. That habit compounds release after release.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">If you want experimentation built into your release pipeline rather than bolted on afterward, EngineerBabu&#8217;s<\/span><a href=\"https:\/\/engineerbabu.com\/services\/mobile-app-development\"> <span style=\"font-weight: 400;\">mobile app development<\/span><\/a><span style=\"font-weight: 400;\"> teams set up flags, exposure tracking, and guardrails from the first sprint.<\/span><\/p>\n<h2><b>About EngineerBabu<\/b><\/h2>\n<p><a href=\"http:\/\/engineerbabu.com\"><span style=\"font-weight: 400;\">EngineerBabu<\/span><\/a><span style=\"font-weight: 400;\"> is a technology development company building products across fintech, healthtech, and AI, from MVPs to scaled, production-ready platforms.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">It holds a CMMI Level 5 rating, has worked with 4 unicorn clients, and has supported 200+ VC-funded products. The company is backed by Vijay Shekhar Sharma.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Founded by Mayank Pratap (Co-founder) \u00b7 mayank@engineerbabu.com<\/span><\/p>\n<h2><b>FAQs<\/b><\/h2>\n<ul>\n<li aria-level=\"1\">\n<h3><b>What is A\/B testing in mobile apps?<\/b><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">It is a controlled experiment where live users are randomly split between two or more app variants, and one pre-chosen metric decides the winner.<\/span><\/p>\n<ul>\n<li aria-level=\"1\">\n<h3><b>How many users do I need for a mobile A\/B test?<\/b><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Roughly 1,000 to 2,000 users per variant to detect a 5% effect. Detecting 2% or 3% effects usually requires 5,000 or more per variant.<\/span><\/p>\n<ul>\n<li aria-level=\"1\">\n<h3><b>How long should a mobile A\/B test run?<\/b><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">At least one full week, and ideally two, in complete weekly cycles. Stop only at the date your sample size calculation is defined.<\/span><\/p>\n<ul>\n<li aria-level=\"1\">\n<h3><b>Can I run an A\/B test without releasing a new app version?<\/b><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Yes, if both variants ship behind remote config or feature flags. Server-side assignment also lets you start and stop tests without store review.<\/span><\/p>\n<ul>\n<li aria-level=\"1\">\n<h3><b>Which tool should a small app team start with?<\/b><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Firebase A\/B Testing, because it is free, already bundled with Firebase, and fast to configure. Move to Statsig or Amplitude as volume and rigor needs grow.<\/span><\/p>\n<ul>\n<li aria-level=\"1\">\n<h3><b>Why do most of my experiments show no difference?<\/b><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Because most do not. Industry data puts mobile win rates near 21%, so flat results are normal and still useful for ruling out bad ideas.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Your last release shipped a redesigned paywall. Revenue moved 4% that week. Was it the paywall, a seasonal dip, or the iOS update that landed the same morning? Without a control group, nobody in that meeting can answer honestly. They can only argue confidently. That gap between a confident answer and a correct one is [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":24531,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1258],"tags":[],"class_list":["post-24530","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-app-development"],"_links":{"self":[{"href":"https:\/\/engineerbabu.com\/blog\/wp-json\/wp\/v2\/posts\/24530","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/engineerbabu.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/engineerbabu.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/engineerbabu.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/engineerbabu.com\/blog\/wp-json\/wp\/v2\/comments?post=24530"}],"version-history":[{"count":1,"href":"https:\/\/engineerbabu.com\/blog\/wp-json\/wp\/v2\/posts\/24530\/revisions"}],"predecessor-version":[{"id":24532,"href":"https:\/\/engineerbabu.com\/blog\/wp-json\/wp\/v2\/posts\/24530\/revisions\/24532"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/engineerbabu.com\/blog\/wp-json\/wp\/v2\/media\/24531"}],"wp:attachment":[{"href":"https:\/\/engineerbabu.com\/blog\/wp-json\/wp\/v2\/media?parent=24530"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/engineerbabu.com\/blog\/wp-json\/wp\/v2\/categories?post=24530"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/engineerbabu.com\/blog\/wp-json\/wp\/v2\/tags?post=24530"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}