Your foundation and approach towards building any product will define the journey of the product. The product will always be known by its capability to match the rising demand for rapid software development and delivery.  

UX/UI testing traditionally is preferred to deal issues with the user interface, ensuring usability, visual appeal, and end-user experience. This often involves manual testing or automated tools simulating user interactions, such as clicking through screens or validating layouts.  

In practice, we see most organizations doing both UI and backend application functionality testing solely through UI testing. This is usually done because many testers are not comfortable testing backend systems without a user-interface.  

This means that when a defect is found in testing – teams first have to determine if it is a UI issue or an bug in the backend system. Chances are, if there was a bug in the backend system, this could have been discovered and fixed much earlier – meaning releases are unnecessarily delayed. 

API testing on the other hand targets the backend systems, validating application programming interfaces (APIs) like REST or GraphQL, which handle data exchange between systems. This approach is faster, capable to automate more, and bypasses the UI layer, making it ideal for continuous integration and continuous deployment (CI/CD) workflows.  

The year 2025 is already being marked by trends such as shift-left testing, AI-driven automation, and continuous testing, all of which emphasize early validation and speed to market. 

But the question isn’t if  you should prioritize API testing—it’s why. 

You need to start questioning why UI/UX testing are failing, why is it not as effective as it used to be. 

The Problem: Why UI/UX Testing Alone Fails in 2025 

UI/UX testing focuses on the surface: buttons, layouts, and user journeys. But modern apps are built on complex, interconnected systems—microservices, third-party APIs, and cloud infrastructure—where core functionality lives beneath the UI. 

✅  Time:  UI/UX tests usually need longer hours compared to API tests. It’s because in a group of people working together, they are also closely interacting with the user interface, digging into the workflows and rendering elements in a browser or application. 

✅  Regular Maintenance:  UI tests frequently break down even due to minor changes in the user interface, like change in CSS selectors, element IDs and page structures.  

Also, they are flaky in nature, leading to false positives ( for better context: tests fail for reasons unrelated to actual bugs).  Debugging them takes a lot of time. Which can directly affect business processes eventually leading to a major failure. 

✅ Complexity:  Creating detailed UI test scripts  for complex applications adds a lot of controlling elements like UI, different browsers, devices, and operating systems. Making it harder to maintain and scale with time. 

Mitigate These Risks and Achieve a Balanced Testing Strategy 

Organizations should adopt the Test Pyramid model, ensuring a strong foundation of unit tests, a substantial middle layer of API/integration tests, and a smaller, focused top layer of UI/End-to-End tests. In practice, we often see the top and bottom layers of the pyramid with little to no middle layer. 

As you move up the pyramid, testing becomes more expensive and time-consuming. That’s why shifting more of your testing efforts to the API layer can significantly reduce costs while improving visibility into critical backend logic and service-to-service communication. 

UI testing—whether manual or automated—should be used strategically: 

  1. Focus on high-impact user journeys

     

  2. Validate new features

     

  3. Run visual regression and cross-browser checks
     

Rather than attempting to test every single UI element. 

Leveraging skilled testers for testing helps to identify usability issues and unexpected behaviors that automated scripts might miss. 

Accessibility testing should be integrated into the development lifecycle to ensure compliance and inclusivity. API testing should be a priority for validating core business logic, data integrity, performance, and security, where it is most efficient and stable. 

And UI automation should be implemented strategically for critical workflows and areas prone to regression to minimize maintenance overhead. 

Finally, implementing continuous monitoring tools in production can help detect UI/UX issues and gather real-time user feedback for quick resolution. 

Why Move to API Testing for Faster Release Cycles?

The primary driver for shifting to API testing is efficiency. UX/UI testing, while essential for user experience, can slow down release cycles due to its dependency on manual processes or complex automation scripts for UI interactions.

API testing, on the other hand, gives a chance for rapid, automated validation of core functionality, such as data flow, response times, and error handling. This helps teams to integrate testing into every phase of development, providing immediate feedback and reducing the time to market.

For instance, continuous testing, as discussed in our Codeless API Testing Whitepaper, involves testing throughout the development lifecycle, from design to deployment, ensuring real-time feedback on risks and quality.

This approach is crucial for achieving faster releases, as it allows teams to address backend issues before your UI is ready and deploy updates more frequently. According to a study by Capgemini, early testing can reduce costs by up to 40% and detect bugs 50% faster, improving collaboration between development and testing teams.

Case in Point : How our Customer a HealthTech Company Rebuilt Quality from the Backend Up

In early 2024, a rapidly growing health-tech provider faced a challenge. Their app was being used by thousands of clinics to collect patient information digitally for eligibility checks and claims processing. They had expanded to over 30 microservices. Each service exposed critical APIs, from identity verification to EHR syncing. 

They were scaling fast after securing Series A funding, but their QA strategy was taking them in circles. Testing revolved around UI workflows: login, form submissions, dashboards. While the app looked polished, bugs kept surfacing late in development cycles, requiring reword from backend developers—especially in areas like insurance lookups and third-party API integrations. In this case, the issues didn’t arise until after deployment. 

The Breaking Point

As usual they planned a critical release, it involved integrating with a new nationwide insurance database. UI tests all passed as they were just basic cases. But after launch, thousands of patient eligibility checks failed silently triggered by an unnoticed API contract mismatch. 

The issue? Upon research it was found that a backend service returned a different response schema. The UI tests never caught it, and patients were stuck.  

They realized their system wasn’t a monolith anymore—it was a mesh of loosely coupled services, all talking via APIs. If they wanted to catch real bugs early, they had to test where the actual interactions happened: at the API layer. 

This marked the beginning of their shift from UI-centric testing to an API-first mindset. 

The Shift: Moving to API-First Testing

Realizing they needed to test their system where the real logic lived—in the APIs—they chose wanted a codeless testing platform. Here’s how it transformed their operations: 

  1. Mapping the System’s Core Workflows

They identified 20+ key services—from appointment scheduling to claims adjudication. By tracing data flows, they discovered hidden interdependencies—like how a failed eligibility check could invalidate downstream billing. 

  1. Testing Without the UI

With qAPI’s process testing tool, they simulated patient workflows like “schedule + verify eligibility + submit claim” entirely through APIs. These tests exposed edge cases—revealing 422 unexpected errors—that UI tests didn’t capture. 

  1. Faster Test Creation via AI

qAPI’s AI engine tested their OpenAPI specs and auto-generated robust test suites. Instead of writing every test manually, the team got a jumpstart on validating error scenarios, invalid payloads, and authentication failures—reducing test creation time by 40%. 

  1. Embedding Tests into CI/CD

They integrated qAPI into their GitHub Actions pipeline. Now, every pull request ran a full process of API tests. If a test failed, it flagged the exact endpoint and payload—speeding up debugging and ensuring only valid code moved forward. 

The Results: Faster Releases, Less Complaints

Within three months of going API-first, the benefits were clear:

✅40% Faster Releases: No more waiting for flaky UI tests to pass.

✅70% Reduction in Flaky Tests: The most fragile UI tests were replaced by stable API validations.

✅Early Detection of Critical Bugs: An issue with token validation in a third-party EHR API was caught pre-staging—saving hours of incident response.

✅Improved Collaboration: Devs and QA started speaking the same language—API testing. They could review test coverage together in planning sessions.

The Big Picture: Why Companies Need to Shift Now

Several benefits emerge from adopting API testing, particularly in the context of faster release cycles: 

✅ Automation and Scalability: API testing is highly automatable, enabling tools to run tests in seconds as part of CI/CD pipelines. This reduces manual effort and ensures scalability, as seen in trends like AI-driven test case generation and self-healing tests, which minimize maintenance overhead  

✅ Early Feedback Loops: Techniques like Test Impact Analysis (TIA) and live unit testing, can rapidly identify code changes affected by recent updates, running only relevant tests. This targeted approach accelerates defect detection and resolution, improving collaboration between developers and testers. 

✅ Quality at the Data Layer: API testing ensures reliability and security at the data exchange level, validating metrics like response times, error rates, and API security.  

This is critical in 2025, with API attacks rising to 27% (up 10% from 2022), and 46% involved in account takeover (up from 35% in 2022), according to Security Boulevard, 2024 

✅ Integration with CI/CD: API testing integrates seamlessly with DevOps practices, enabling continuous testing throughout development. Our insights have clearly shown that automation tools can support agile environments, ensuring stability with every code change, which is vital for frequent releases. 

Trends Supporting the Transition in 2025

Several 2025 trends support the move to API testing:

✅ Shift-Left Testing: This approach, gaining momentum, prioritizes testing early in the development cycle, often at the API level. It reduces costs, detects bugs early, and fosters collaboration between development and testing teams.

✅ AI-Driven Automation: AI tools optimize test creation, execution, and maintenance, with 42% of IT professionals at large organizations actively deploying AI, according to IBM. This enhances speed and efficiency, particularly for API testing, by predicting scenarios and automating defect tracking.

✅ Continuous Testing and Feedback Loops: The focus on continuous testing, as seen in Testleaf’s analysis, ensures real-time feedback, enabling faster iterations without compromising quality.

✅ Low-Code/No-Code Solutions: Emerging tools, potentially aligning with quality-focused API testing frameworks, helps testing by enabling non-technical users to create and execute API tests.  Gartner predicts that by the end of 2025, 65% of application development will involve low-code/no-code platforms, aligning with quality-focused API testing frameworks.

Architectural Transformation: The Impact of Microservices on the Evolution of API Testing

The transition from monolithic systems to microservices has revolutionized the way APIs are built, tested, and deployed. Each microservice acts as a self-contained unit, communicating via APIs—which means API testing is no longer optional. 

With more moving parts, the complexity of API testing rises exponentially. Managing inter-service dependencies, ensuring schema contracts are upheld, testing across fragmented data sources, and aligning distributed teams are just a few challenges this architecture introduces. 

qAPI was built to address this exact problem. It’s tailored for testing in distributed, high-scale environments. Our AI testing features ensure changes don’t break downstream services, while service virtualization and mocking let you test even when parts of your system are incomplete or under maintenance. 

The Test Pyramid Evolved: Why Traditional Testing Models Fall Short 

The “Old Test Pyramid”—with unit tests at the base, integration in the middle, and UI tests on top—worked well for monoliths. But in today’s API-first, microservice-heavy architectures, it needs serious restructuring. 

Modern approaches like the “Testing Honeycomb” recognize this shift. With dozens of services communicating across APIs, integration and contract testing take center stage, often forming the bulk of your testing effort. 

qAPI supports this evolution by offering pre-built templates for integration tests, process testing tools, and performance test cases for testing multiple service interactions. This lets you test entire business workflows—without worrying about fragile UI tests. 

Did you know? Companies who implement robust integration testing strategies see a 48% reduction in production incidents related to broken service dependencies. 

Speed Meets Stability: Continuous Testing in CI/CD Pipelines 

Now everyone’s code is deployed independently—and frequently. This means every service update could potentially break another. To prevent regressions, API tests need to be embedded directly into your CI/CD process. 

With qAPI, you can integrate seamlessly into GitHub Actions, GitLab, or Jenkins pipelines. Our automated test generation means developers spend less time writing boilerplate test cases and more time building features. 

Even better, non-engineering team members can run tests with our codeless UI—making it easier to democratize quality and increase test coverage without inducing developer bias. 

Rebuilding Processes: How Should Teams Move Forward 

Here’s the truth: software quality isn’t just about catching bugs—it’s clearly about building processes that scale. Most companies that survived turbulent times didn’t just have the right talent; they had the right system in place. 

By the end of 2025, 70% of enterprise apps will use microservices, per IDC, with 40% still lacking traditional UIs. API testing ensures quality for these systems, while UX/UI testing applies to only 60% of use cases. 

As we move closer to 2030, software teams will face dual pressures to ship faster and ensure stability. Microservices provides them agility, but they also multiply the surface area for bugs, regressions, and failures. With qAPI, your team spends less time babysitting tests and more time shipping great software by: 

✅ Automate repetitive testing 

✅ Ensure API compatibility across versions 

✅ Accelerate delivery cycles 

✅ Future-proof their software architecture 

✅ Shift testing left, so issues are caught earlier

✅ Unify QA, DevOps, and product teams under one testing strategy

Final Word: UI Testing Isn’t Dead—It’s Just Not Enough 

Organizations that successfully optimize their software delivery processes, achieving the needed combination of speed, efficiency, and good application quality will not only move faster—they’ll build software that lasts. 

You still need to test the user experience. But your product isn’t just the UI—it’s the data pipelines, the APIs, the machine learning predictions, the third-party integrations. That’s where failure lives. And that’s where your testing should begin. 

Our client’s transformation proves it: with the right tooling and approach shift, even highly regulated, complex industries like healthcare can ship faster and safer. 

Ready to build APIs that scale and ship without fear? Check out our Must-Have API Testing Guide and see how qAPI can help you modernize your entire testing approach. 

Table: Comparison of UX/UI Testing vs. API Testing for 2025 Release Cycles 

Aspect UX/UI Testing API Testing
Focus User interface, usability, visual appeal Backend data exchange, functionality
Speed Slower, often manual or complex automation Faster, highly automatable, CI/CD friendly
Automation Level Moderate, UI interactions can be complex High, runs in seconds, scalable
Feedback Loop Delayed, post-development testing Early, continuous feedback via pipelines
Quality Metrics UI bugs, user experience Response times, security, error handling
2025 Trends Shift-right, manual validation Shift-left, AI-driven, continuous testing
Cost Higher initial costs due to manual testing and complex automation tools (e.g., Selenium, Cypress). Maintenance of UI scripts is resource-intensive, with 20-30% of testing budget spent on updates, per industry reports. Lower long-term costs due to high automation and reusable scripts. Early defect detection reduces rework costs by up to 30%. Initial setup may require investment in CI/CD integration.
Save 100+ Testing Hours/Year Our Guide to Codeless API Test Automation    

Let me introduce you to Emily and her professional world. Emily is a seasoned API developer at a Nationalized Bank, a mid-sized retail bank with millions of customers across urban and rural branches. Emily’s job isn’t just limited to coding— she also takes care of the digital wireframe of the bank that processes thousands of transactions daily, from salary deposits to loan repayments.  

There are multiple APIs in place that power everything, from the mobile app that rural farmers use to check microloan balances to the online portal where people in the city pay their credit card bills. 

But we all know that in banking, the challenges are as unique as the customers you serve, and it can have a direct impact on their customers’ livelihoods.  

Recently, a critical bug slipped into production. Customers were reporting that their orders were being duplicated, causing confusion and frustration. Upon investigation, Emily’s team discovered that the root cause was a failure in the API responsible for processing payments. 

The API, which communicated with the payment gateway, was frequently timing out and causing duplicate requests to be sent.  

Had Emily’s team planned for a thorough API testing protocol, this bug could have been caught early. The incident was a wake-up call—as API testing was not just an option; it was now essential. 

In this article, we’ll dive into the value of API testing features, showing how they tackle real-world challenges in banking, financial services, and insurance (BFSI).  

And how it can completely change the outcome for such scenarios. 

The High-Stakes Reality of Retail Banking APIs 

Retail banking isn’t just about moving money—it’s about trust, accessibility, and real-time reliability. Emily and her team were using APIs that connected a farmer’s smartphone to a loan disbursement system, or a busy parent’s browser to a bill payment gateway.  

When they work, no one notices. When they fail, the fallout is immediate: a delayed loan disbursement could mean a missed planting season, and a slow payment portal could really affect customer experience. In this sector, APIs aren’t just technology based—they’re the lifeline of financial transactions and customer loyalty. 

Performance issues aren’t new for Emily. Like a sudden surge in loan applications during harvest season, led to payment failures on payday when thousands of transactions hit at once, or an overloaded API causing delays in real-time account updates. These can’t just cause an inconvenience—it directly affects customer trust and banking reliability. 

Let’s rewind. What if Emily’s team had the right testing approach from the start? 

It begins with choosing the right tool and processes to test the API-layer, tools like qAPI make this easy to create, scale, and maintain. With the right tool in place teams can begin importing APIs efficiently.  

Whether working with Swagger files, Postman collections, WSDLs, or even manual entries, having all APIs imported into a unified platform ensures no endpoint is missed. This step is critical — without it, teams waste time setting up tests manually or risk incomplete coverage. 

After importing the endpoints, the team should have planned for writing test cases – many tools require users to code these themselves, however qAPI provides codeless assertions and even AI solutions that provide auto-generated test cases 

This ensures each API is validated — checking requests, responses, and error handling — without needing to write complex scripts. For example, a payment API could be tested not only for successful transactions but also for edge cases like timeouts, retries, and partial failures. 

For Emily, this could have mapped the payment processor’s API fast, flagging functional bugs minutes after a release to lower environments, rather than waiting for them to create problems with customers. 

Codeless Testing for APIs 

With APIs imported, Emily’s next challenge was testing under pressure. Not everyone on her team was a good coder, but all needed to pitch in. Business analysts, compliance officers, and even product managers needed to validate API behavior without writing complex code.  This is where codeless API testing becomes a game-changer.  

With an intuitive, drag-and-drop interface, anyone on Emily’s team could: 

✅ Select an endpoint—no manual scripting required 

✅ Set parameters and define expected responses 

✅ Run automated tests with zero coding skills 

By leveraging codeless testing, Emily’s team significantly accelerated test execution, ensuring APIs were validated for both functional tests and process tests. 

Codeless Assertions 

Running tests is half the battle—verifying results is the rest. Codeless assertions streamline this by- 

Validating API response headers, response bodies, JSON Paths, and schemas can all be verified through a few clicks — no scripting required.  

Users can even build many test cases for one endpoint to ensure there is proper coverage – validate a login endpoint with incorrect usernames, passwords, or invalid characters to ensure proper functionality across all scenarios. 

This ensures that Emily’s team maintains high test coverage, quickly spotting any faults without diving into complex code. 

Transitioning from securing APIs to handling unpredictable traffic spikes, Emily’s next challenge requires not just stability, but scale. 

Building Scalable APIs—Defending Against the Invisible Enemy 

Forget generic “cyber threats.” Emily’s nightmares are specific: Her bank supports a government microloan scheme for farmers, and every change in season, thousands flood the system to check balances.  

Last year, 50,000 simultaneous hits crashed the API, causing a 20-minute outage. Branches were overwhelmed with calls, and customers were furious. 

Retail banking APIs handle loads of sensitive data—think Aadhaar numbers in India, Social Security number in the U.S., or transaction histories that reveal a customer’s entire financial life.  

There have been several cases like that. In February 2025, DeepSeek, faced a massive server resource constraints due to a sudden spike in global demand for their services. This unexpected surge led to server issues over a two-week period, ultimately forcing DeepSeek to temporarily suspend API service top-ups. 

This shows the critical need for scalable and resilient API infrastructures to handle unpredictable traffic patterns. Similarly, Emily and her team needs to prepare their APIs for the worst. 

She should use a tool that can run performance tests by simulating real-world surges —replicating 50,000 farmers checking balances simultaneously — while factoring in the bank’s aging mainframe’s limitations. It can detect a slow database query. Emily could have tested the fix, cutting response times from by at least 50% — just in time for the next spike in demand for microloans. 

Enabling AI for API Testing 

In the BFSI sector, maintaining regulatory compliance is complex and equally important. Financial institutions are under constant supervision to follow to evolving regulations aimed at ensuring consumer protection, financial stability, and market integrity.  

In 2024, a leading European bank faced a €14 million fine when its loan approval API misinterpreted data, unintentionally offering loans to unqualified applicants — a direct violation of the region’s fair lending regulations. 

qAPI offers automated testing, real-time monitoring, and AI-assisted risk assessments, functional tests ensuring Emily’s team stays ahead of regulatory changes. With AI-powered assertions, the platform automatically validates API responses against regulatory criteria — like ensuring loan approvals return compliant, unbiased decisions. 

If Emily configures qAPI to verify that every API handling credit application in line to the bank’s fair lending policies — checking that approvals don’t factor in non-permissible data like postal codes or wrong sender data. If the API response deviates, qAPI flags it instantly, preventing non-compliant actions from reaching customers. 

Better yet, qAPI can generate audit-ready reports with timestamps and response rate, helping Emily’s prep time from weeks to hours. When the next vigilance inspector knocks, she’ll hand over a binder that’s airtight—and impress them with all of the saved test reports from qAPI. 

Keeping Systems Online and Intact

Here’s how qAPI’s AI-powered assertions could’ve prevented this from day one.

  1. Credit Approvals: Emily configures AI assertions to validate that loan approvals align with the bank’s policies. qAPI continuously checks whether critical data fields — like income source or repayment history — are correctly processed. If a regulatory update changes approval criteria, in qAPI  you can update the test cases instantly and automate the tests, ensuring the API remains compliant.

  2. Fraud Detection: The bank relies on fraud detection APIs to monitor transactions. With qAPI in place, Emily’s team can configure a test that can ensure that these APIs validated to flag nefarious activity (e.g., farmers buying fertilizers in bulk). The AI learns from past behavior, raising alerts only on true anomalies — reducing false red flags that can frustrate customers.

  3. Payment Gateways: Failed payments mean both financial losses, potential regulatory penalties, and lost customers. qAPI facilitates continuous testing of payment APIs across cards and net banking — simulating timeouts, retries, and partial payments — ensuring seamless, regulation-compliant transactions every time.

Why It All Matters: Building Trust, One Test at a Time

For banking service providers, whether it’s processing payments, detecting fraud, or meeting regulatory demands, the difference between smooth operations and costly chaos often comes down to one thing: having the right protocols in place.

With qAPI’s codeless framework, you don’t need to be a coding wizard to set up these tests. A tester can drag, drop, and run a payment flow in minutes, ensuring every handshake between systems works like clockwork. All you need to understand is the application you are testing.

API testing lets us throw every possible fraud scenario at the system: unusual patterns, rapid-fire transactions, you name it. qAPI makes it practical, letting even non-technical team members build and automate these tests.

The result? A fraud detection system that’s as tough as the threats it’s up against. It’s fast—set up a test suite in hours, not days—and proactive, catching bugs before they hit production.

Emily’s story is a clear reflection of the expectations in retail banking

With qAPI in her toolkit, the narrative shifts:

  1. Security breaches are intercepted before they surface, with codeless, data-driven tests keeping APIs airtight.

  2. Traffic surges become predictable, with performance simulations ensuring the system holds strong — even under monsoon microloan spikes or unexpected viral demand.

  3. Compliance evolves from a reactive scramble to a proactive, AI-backed process — preventing errors before they escalate into regulatory problems.

  4. AI-assisted assertions help Emily’s team validate API responses faster and more accurately, reducing time spent on manual testing.

  5. Cloud-based scalability ensures that APIs perform under real-world loads, simulating thousands of concurrent users effortlessly.

  6. Support for multiple API types enables seamless testing of REST, GraphQL, and SOAP APIs while integrating easily into existing workflows.

The qAPI Discovery Chrome Plugin helps extract and automate API test cases and workflows directly from web applications, increasing coverage and efficiency.

Real-world test scenarios with API chaining and data plumbing ensure that test cases reflect actual user interactions, making API reliability a possibility.

With the right codeless API testing solution in your toolkit, you’re not just keeping up; you’re crafting workflows that bend to your will, tailored to your data, and designed to fit your unique challenges.

Remember that automating tests isn’t a set-it-and-forget-it task — it’s an ongoing process of improvement, and with qAPI you can stay a step ahead.

Get started today!

Save upto 100+ Testing Hrs/Yr   With Our Guide to Codeless API Test Automation    

Overview 

The industry’s first neo-bank was launched in the UK to specifically cater to the demands of affluent clients—approximately 7 million professionals, entrepreneurs, property investors, and others. With a mission to deliver hyper-personalized financial products (e.g., savings, fixed deposits, lending), their growth was always dependent on rapid software releases.

The Challenge 

A Need for Change 

For any financial institution, trust is built on reliability. Our client, a leader in digital banking and financial services, prided itself on delivering seamless customer experiences. However, as their digital offerings expanded, so did the challenges of maintaining software quality.  

The institution found itself at a crossroads—struggling with lengthy testing cycles, defect leakage, and the increasing pressure to release updates faster while maintaining impeccable reliability. 

Despite the best efforts of their development and quality assurance teams, traditional manual testing methods were falling short. Bugs that escaped detection in QA often came back in User Acceptance Testing (UAT), leading to costly fixes and delays. 

With customer expectations at an all-time high, the institution took a strategic approach to rethink its API testing metrics. 

The Objective

The Roadblocks to Innovation

The bank’s testing inefficiencies stemmed from several key pain points:
  1. Heavy Reliance on Manual Testing: The majority of test scenarios required manual execution, making regression testing time-consuming and resource-intensive.

  2. Defect Leakage: Up to 65% of defects made it past QA undetected, only surfacing in UAT, increasing remediation costs and time.

  3. Extended Testing Timelines: Some releases required up to 17 weeks of testing, delaying critical updates.

  4. Scalability Challenges: As digital services expanded, the existing test framework struggled to keep up.

Recognizing that speed and accuracy were important for them, our client the bank planned for a solution that could streamline its testing efforts without sacrificing quality. 

The Solution

A Shift to AI-Powered Test Automation

The bank got a complete walkthrough of qAPI—a next-generation, AI-driven test automation platform. Designed to seamlessly integrate with existing workflows, qAPI offered a scalable, codeless solution that would transform the institution’s testing strategy. With qAPI, the bank was able to:
  1. Automate Complex Test Scenarios: The team developed over 110 automated test scripts tailored to complex loan processing workflows, ensuring comprehensive validation.

  2. Reduce Defect Leakage: The defect leakage rate from QA to UAT dropped from 65% to just 10%.

  3. Accelerate Test Cycles: Testing timelines were reduced from 17 weeks in Release 4 (R4) to just 5 weeks in Release 9 (R9), a 70% reduction in test cycle times.

  4. Optimize Testing Efforts: Manual testing efforts in UAT were cut by 80%, allowing testers to focus on more strategic tasks.

  5. Enhance Test Execution with AI: qAPI’ AI-powered platform enabled early bug detection, reducing last-minute defects and rework.

The Solution 

A Shift to AI-Powered Test Automation 

The bank got a complete walkthrough of qAPI—a next-generation, AI-driven test automation platform. Designed to seamlessly integrate with existing workflows, qAPI offered a scalable, codeless solution that would transform the institution’s testing strategy. With qAPI, the bank was able to: 

  1. Automate Complex Test Scenarios: The team developed over 110 automated test scripts tailored to complex loan processing workflows, ensuring comprehensive validation. 
  2. Reduce Defect Leakage: The defect leakage rate from QA to UAT dropped from 65% to just 10%.
  3. Accelerate Test Cycles: Testing timelines were reduced from 17 weeks in Release 4 (R4) to just 5 weeks in Release 9 (R9), a 70% reduction in test cycle times.
  4. Optimize Testing Efforts: Manual testing efforts in UAT were cut by 80%, allowing testers to focus on more strategic tasks.
  5. Enhance Test Execution with AI: qAPI’ AI-powered platform enabled early bug detection, reducing last-minute defects and rework.
The Impact

Delivering Measurable Impact

The implementation of qAPI transformed the financial institution’s approach to testing, yielding substantial benefits:
  1. Enhanced Efficiency: The institution automated over 550 test cases, achieving 90% test script execution on qAPI.

  2. Higher Software Quality: Early-stage bug detection led to fewer production defects and improved software stability.

  3. Reduced Operational Costs: The shift from manual to automated testing significantly lowered costs.

  4. Scalability & Security Compliance: qAPI’ cloud-based, ISO 27001 & SOC2-compliant infrastructure ensured a secure and scalable testing environment.

  5. Seamless Integration: With its intuitive, codeless interface, qAPI eliminated the need for costly custom frameworks, making adoption seamless.

Qyrus’s all-in-one platform combines AI intelligence, codeless automation, and enterprise-grade security to empower teams across the testing lifecycle: 

  1. Unified Testing: Complete end-to-end API testing on a single cloud platform. 
  2. Zero Maintenance: Pre-configured infrastructure with no setup time. 
  3. Cost Efficiency: Reduced operating costs and human errors by up to 40%. 
Read More : How Organizations Can Leverage AI and Automation to Transform API Testing beyond Developer Centric Approach 

About qAPI :

qAPI is the industry’s first End-to-End API Testing company that helps organizations achieve API protection in a cloud-first, API-driven world. We help businesses with innovative tools and services designed to streamline API testing, ensure reliability, and enhance application performance. Trusted by financial institutions, logistics companies, and many more worldwide, we help organizations create products and APIs they can depend on for seamless performance and integration. 

To learn more about our products and services, visit us at www.qyrus.com/qapi/ 

Overview 

Our client has cemented its position as an industry leader by providing to timely services and maintaining public health standards for over a decade. As one of the leading North American waste handling companies, with over 50,000 employees and $15.2 billion in revenue, they recognized the need to enhance its software systems to manage tasks such as collections, waste disposal, logistics, inventory, and supply chain distribution.  

The Challenge 

Breaking Free from Legacy Testing Limitations 

As customer expectations evolved and operational demands surged, the company needed to rethink how it built, tested, and deployed its technology. Instead of relying on traditional methods, they adopted a data-centric, outcome-driven approach to overcome testing challenges. 

The digital ecosystem was built on a complex network of APIs and microservices, powering everything from customer portals to logistics management and even legacy applications. The company had two critical applications driving its day-to-day operations: a customer-facing e-commerce platform and an internal business process application.  

These systems powered everything from service requests to route optimizations, and they were evolving fast. The problem? Testing wasn’t keeping up and cost the team a lot of time. 

Manual API testing had once been sufficient, but with the explosion of new endpoints and frequent updates, the cracks started to show: 

  1. Test Coverage Gaps: The team could only validate a fraction of their API interactions, leaving potential defects unnoticed. The testing infrastructure struggled to handle the growing complexity of their API ecosystem. Traditional tools were not designed to test the intricate interactions between microservices effectively.
  2.  Slow Releases, Growing Risks: Each new release carried the risk of undiscovered bugs, as they were often discovered late in the development cycle affecting performance.
  3.  Resource Strain: With engineers stretched thin, the testing process was eating into development time, forcing teams to choose between speed and quality. 

For a company built on efficiency, this was a roadblock they couldn’t afford. 

The Objective 

Redefining The Testing Approach 

The company started to explore on the possibility of a digital transformation journey, heavily relying on API enhancement to streamline workflows. 

The leadership team knew they had to break free from the limitations of manual testing. Their goal was to implement a testing solution that could: 

  1. Accelerate Testing Cycles: Reduce the time required for API testing without compromising on quality.
  2. Improve Test Coverage: Ensure comprehensive validation of API interactions, including edge cases and error handling.
  3. Accelerate deployment cycles without sacrificing quality, ensuring every update improved—not jeopardized—user experience.
  4. Scale with Growth: Build a testing framework capable of handling the increasing complexity of their microservices architecture. 

After seeing qAPI seamlessly integrate into their processes and bring a refreshing change of pace, the waste handling company knew it was a trusted solution. Now, they were ready to turn this vision into reality. 

The Solution 

Intelligent Automation, Real-Time Insights 

With qAPI’s AI-powered, codeless API testing platform the company was able to create customized workflows designed to eliminate bottlenecks and supercharge testing capabilities.  

By leveraging automated testing, the company established a structured testing framework that enabled seamless collaboration across teams. This approach accelerated the detection and resolution of defects throughout the service journey, ensuring a more natural and effective development progression. 

The implementation helped deploy: 

  1. 4,500 Automated Test Scenarios: Within four months, the company automated thousands of test cases, covering 400+ API endpoints across SOAP and REST architectures.
  2. Dynamic Data Injection: Unlike traditional scripts, qAPI leveraged unique, randomly generated data to simulate real-world interactions and uncover hidden vulnerabilities.
  3. Seamless CI/CD Integration: The platform integrated directly into the company’s development pipelines, enabling automated test execution with every new build. With this process in place, teams had a comprehensive report of all of their systems within minutes of finishing a new build.
  4. Scalability Beyond APIs: Encouraged by success, the company expanded automation efforts into mobile and web testing, to eventually convert into a unified testing ecosystem.
  5. qAPI Process Testing: Integrations between different applications and systems were validated after being triggered after a new build.

The Impact

Testing The Impact of Custom API Testing Strategy 

  1. Accelerated Release Cycles: Automated API testing reduced testing time by 40%, enabling faster deployment of new features and updates.
  2. Higher Software Quality: Comprehensive test coverage and early bug detection resulted in a 30% reduction in production defects, enhancing software stability and reliability.
  3. Seamless Team Collaboration: A unified testing platform improved communication and coordination across development teams, reducing misalignment and increasing efficiency.
  4. Scalability for Growth: qAPI’s scalable infrastructure ensured that the company’s testing framework could expand alongside its business, supporting long-term digital transformation.
  5. Operational Cost Savings: By minimizing manual testing efforts and late-stage defect resolution, the company significantly reduced development and maintenance costs. 

About qAPI 

qAPI is the industry’s first End-to-End API Testing company that helps organizations achieve API protection in a cloud-first, API-driven world. We help businesses with innovative tools and services designed to streamline API testing, ensure reliability, and enhance application performance. Trusted by financial institutions, logistics companies, and many more worldwide, we help organizations create products and APIs they can depend on for seamless performance and integration. 

To learn more about our qAPI. 

You’re spending a lot of time testing APIs and under different conditions. With every test you run, the success rate you achieve, there are some learnings along the way that make you realize lot more than you usually account for. 

Consider a rapidly growing company working towards refining its digital infrastructure. Their engineering team must make an important decision, on picking the API strategy that supports expansion while maintaining performance and flexibility.  

REST has been the industry-standard for long, but GraphQL is gaining traction for its precise data-fetching capabilities. SOAP, though considered legacy, remains the backbone of industries like banking and healthcare due to its reliability and security. Meanwhile, event-driven architectures, powered by Streams, are transforming real-time data processing. 

The Challenge? 

Each approach has trade-offs—REST is simple but can often exposure to multiple inefficiencies in data retrieval. GraphQL offers flexibility but adds complexity, SOAP on the other hand ensures strict compliance but feels rigid in modern agile environments. And Streams enable real-time interactions but requires long list of efforts to make a shift in system design. Organizations must make these choices wisely, to balance performance, security, and scalability. 

API testing differs from the tasks you need to execute to validate functionality and performance, rather than just verifying outputs. It involves understanding the nuances of data transmission, error handling, and system interactions, helping you refine your approach to scalable testing and automation. 

Learn about the four popular API paradigms—REST API, SOAP, GraphQL, and Streams —to you developers and testers understand their relevance, functionality, and when to use them. By the end, you’ll have a clearer picture of which API approach aligns best with your product, your users, and your long-term vision. Let’s dive in. 

What are REST, GraphQL, Streams and SOAP? 

All these are different types of APIs each created to serve a unique proposition and use case. From building the right mobile application to integrating microservices, having the right API architecture in place makes all the difference. 

At a time where code makes or breaks a product it’s important to know what they are, what they can do before you figure out how to use them. 

  1. What is REST API (Representational State Transfer) 

REST API is an architectural template introduced in the 2000s built to be easy to use and implement. It uses the standard HTTP methods (GET, POST, PUT, DELETE) to interact with resources and design networked applications. 

REST APIs support caching mechanisms (via HTTP headers like Cache-Control and ETag) to reduce the load on the server. And majorly use JSON or XML for data exchange. 

The reason why it’s used widely is REST’s stateless nature making it a good fit for applications that need to grow and handle high traffic. 

Ideal use case: Web and mobile apps, public APIs, microservices. 

Example: You can search the user data from a server using a GET request to /users/1. 

How does a REST API work: REST APIs uses standard HTTP methods to execute CRUD (Create, Read, Update, Delete) operations. Here each request is stateless, as it contains all necessary information for processing, and responses are in JSON or XML format. 

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What-are-REST-GraphQL-Streams-and-SOAP-Rest-API

What is SOAP (Simple Object Access Protocol)  

SOAP was introduced in the late 1990s and is one of the oldest protocols used for exchanging structured information in distributed systems. The intent was to design and establish strict standards for enterprise-level communication. 

SOAP has advantages in industries that need high security and reliability, such as banking, healthcare, and government entities. The rigid structure on SOAP provides consistency and compliance with standards like WS-Security. 

Ideal Use Case: Enterprise applications, financial transactions, legacy systems. 

Example: You can create a SOAP request to a banking service to initiate transfer funds: 

How does SOAP API work: SOAP operates on an  XML-based messaging format and requires a strong protocol specification. It supports ACID* transactions, built-in error handling, and enterprise-grade security features. 

SOAP Uses WSDL (Web Services Description Language) for defining operations. 

*FYI- ACID stands for Atomicity, Consistency, Isolation, and Durability—it’s a set of features that help maintain data integrity, especially in enterprise applications dealing with sensitive or financial data. 

What is GraphQL  

GraphQL was developed and launched in the market by Facebook in 2015 to address inefficiencies that were found in the REST APIs, aiming to solve issues related to over-fetching and under-fetching of data. It helped users to request exactly what they needed, while eliminating the need for unnecessary data transfer. 

GraphQL  grew its popularity in mobile and web applications as the need for dynamic data requirements were crucial in these segments. It’s ideally useful for complex queries and real-time updates. 

You can easily use a single endpoint (/graphql) instead of multiple routes. 

For example: A user can write a request to server for a user’s name and email:

How does GraphQL work: GraphQL enables clients to request precisely the data they need through a structured query language. Instead of multiple API calls, clients can send a single query specifying exactly what they need.

Streams 

Streams were developed as a resource to handle continuous data flow in real-time for applications. As traditional APIs operated on request-response models, Streams are capable of enabling constant data transmission within separate systems. 

Streams pushes data to clients as it becomes available. It’s put into action by using technologies like WebSockets, Server-Sent Events (SSE), Kafka, or gRPC Streams. 

How does streams work? 

  1. Connection Established – The client subscribes to a data stream using protocols like WebSockets, Server-Sent Events (SSE), or Kafka. 

  2. Continuous Data Flow – Once connected, the server pushes data to the client in real time whenever new data is available.
     
  3. Event-Driven Updates – The stream automatically updates the client when new information arrives, eliminating the need for repeated API requests (polling). 

  4. Handling & Processing – The client processes incoming data immediately, whether it’s a stock price update, chat message, or live sensor data. 

  5. Closing the Stream – When no longer needed, the client or server can terminate the connection to stop the data flow. 

Now you might get confused as even GraphQL has the same functionality, but the difference lies in the approach. 

Key Differences Between GraphQL and Streams 

             Feature
 GraphQL Streams
Underlying Mechanism  Built on WebSockets to push updates from the server when data changes.  Uses various streaming protocols like WebSockets, SSE, Kafka, or gRPC for continuous or event-driven data transmission. 
Data Flow  Clients subscribe to specific changes (e.g., updates to a particular entity).  Data flows continuously (like a live feed) or is pushed based on events. 
Use Case  Best for applications where clients need to get real-time updates on specific queries (e.g., chat apps, stock price updates).  Ideal for high-throughput, event-driven, or real-time streaming use cases (e.g., video streaming, IoT, analytics pipelines). 
Scalability  Can be complex to scale due to WebSocket limitations. Requires additional infrastructure for large-scale deployments.  Designed for high-scale real-time processing (e.g., Kafka handles millions of messages per second). 
State Management  Works well for structured, stateful real-time updates.  Works for both stateful and stateless event-driven architectures. 

When to use SOAP vs REST vs GraphQL vs Streams? 

When should you use REST API: 

  1. When you are building scalable web applications that require stateless communication. 

  2. For public-facing APIs used for third-party integrations. 

  3. Applications that will benefit from caching mechanisms for performance optimization. 

Example: Social media platforms like Twitter use REST APIs for fetching tweets, posting updates, and managing user profiles. 

When should you use SOAP: 

  1. Enterprise-level applications that need high security along compliance.
     
  2. For building systems handling financial transactions or sensitive data. 

  3. And also, legacy systems that need to abide by well-defined standards. 

Example: Payment gateways like PayPal heavily use SOAP APIs to secure money transactions, ensuring integrity and security. 

When should you use GraphQL: 

  1. For mobile and web applications that require efficient data fetching with minimal over-fetching/under-fetching.
     
  2. Complex queries involving nested relationships across multiple entities. 

  3. If you need real-time visibility through dashboards and personalized content delivery. 

Example: GitHub uses GraphQL APIs to allow developers to fetch repository details, issues, and pull requests in a single query, improving efficiency. 

When should you use Streams: 

  1. For real-time monitoring systems, such as IoT sensors and stock market applications. 

  2. Event-driven architectures like chat applications or live notifications. 

  3. To build and manage big data pipelines that need continuous processing. 

Example: Netflix leverages streaming APIs to deliver video content in real-time, ensuring seamless playback and adaptive streaming. 

  REST SOAP GraphQL Streams
Design  Exposes data/resources via URIs. Focuses on CRUD operations (Create, Read, Update, Delete).  Exposes operations (functions or actions) via a strict contract. Focuses on service actions.  Exposes a flexible query system that allows clients to request exactly the data they need.  Exposes a continuous stream of events or data, often in real-time, through a persistent connection. 
Transport Protocol  HTTP/HTTPS  HTTP/HTTPS, SMTP, JMS, etc.  HTTP/HTTPS  WebSockets, HTTP/2, MQTT, gRPC 
Data Format  Typically, JSON or XML  XML  JSON (typically)  Varies (JSON, Avro, Protobuf, etc.) 
Performance  Generally fast, lightweight, stateless, scalable.  Slower due to XML parsing and overhead.  Can be efficient but depends on query design (over-fetching, under-fetching).  High performance for real-time, but latency can vary based on connection. 
Scalability  Easily scalable with caching and load balancing.  Less scalable due to heavier processing overhead.  Highly scalable with efficient query design.  Highly scalable, especially in real-time environments. 
Security  Typically relies on HTTPS, OAuth, and API keys.  Built-in WS-Security, SSL, and additional standards.  Relies on HTTPS, OAuth, and custom access control.  Security relies on the transport layer (e.g., SSL/TLS). 

How to handle these APIs under different conditions

REST API Testing Best Practices: 

Validate Endpoint Configurations: 

  1. Ensure that all API routes are correctly defined with the proper URL structures. 

  2. Check for missing or misconfigured routes, which can result in broken functionality or unexpected failures during API calls. 

Ensure Correct HTTP Status Codes: 

  1. Verify that the API returns the appropriate HTTP status codes for different scenarios (e.g., 200 OK for successful requests, 400 Bad Request for client-side errors, 500 Internal Server Error for server-side issues). 

  2. Incorrect status codes can cause misinterpretations by the client and complicate debugging. 

Implement Efficient Caching: 

  1. Test caching mechanisms to ensure reduced database load and optimized response times. 

  2. Ensure cache invalidation is handled properly, preventing unnecessary data retrievals from the database on repeated requests. 

SOAP API Testing Best Practices: 

XML Schema Validation: 

  1. Confirm that XML requests and responses conform to the defined XSD schema. 
  2. Validate the presence and correctness of required fields in the XML payloads to avoid parsing errors. 

Verify Required Headers: 

  1. Ensure that essential headers, such as authentication tokens and content-type metadata, are properly included in API requests. 
  2. Missing headers can result in authentication failures, permission issues, or malformed requests. 

Test for Security Flaws: 

  1. Perform security testing to identify weak encryption protocols, inadequate input sanitization, or improper authorization checks. 
  2. Ensure sensitive data is transmitted securely and that access control mechanisms are strictly enforced. 

GraphQL API Testing Best Practices: 

Test for Efficient Query Handling: 

  1. Ensure the server can efficiently handle deeply nested queries without triggering performance bottlenecks, such as high CPU usage or timeouts. 
  2. Test for graceful handling of complex queries to prevent server crashes under heavy loads. 

Validate Resolver Logic and Data Integrity: 

  1. Test resolver functions to ensure they return correct and complete data according to the schema, preventing data inconsistencies or incomplete responses. 

Optimize Query Efficiency: 

  1. Design queries to avoid over-fetching (retrieving excessive data) or under-fetching (missing essential data) from the server. 
  2. Monitor and optimize response payload sizes to maintain performance and reduce the client-side processing burden. 

Streams API Testing Best Practices: 

Check Connection Stability: 

  1. Test how the stream handles persistent connections under high network traffic or fluctuating network conditions. 
  2. Ensure that connection dropouts or instability don’t interfere with the real-time data flow. 

Avoid Duplicate Event Handling: 

  1. Test the system’s ability to handle and de-duplicate events in the message stream to prevent data inconsistencies and avoid redundant processing. 

Minimize Latency: 

  1. Measure the latency in real-time data delivery, and test for any delays that could impact user experience in time-sensitive applications, such as financial systems or IoT devices. 
  2. Ensure that the system performs optimally even with network congestion or high event volume. 

How can qAPI support your API testing requirements? 

qAPI offers end-to-end API testing services to support your growing API needs. Our cloud-based application is a fully managed service that makes it easy for developers/testers/QA teams to test, monitor and secure APIs of all types and sizes.  

Here are some ways you can benefit from using qAPI. 

  1. AI generated test cases for your APIs 

  2. Automated workflows that are completely customizable. 

  3. Performance and functional testing simulations with real-time visibility 

  4. Run multiple iterations of same APIs to release new versions with ease. 

Get started with qAPI by creating a free account today. 

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As of 2025 the software development industry has gone through a drastic shift. Agile methodologies are now being actively replaced by the rigid waterfall approach, followed by changes in iterative development, customer collaboration, and rapid deployment.  

The two-week sprints are now becoming the gold standard. Developers wrote code, testers validated it, and releases happened every few weeks. This was the future—until it wasn’t. 

Fast-forward to today.  

Industry leaders like Netflix and Facebook are deploying API changes hundreds of times per day. Amazon deploys updates every 11.7 seconds on average. 

Numbers like that are exactly why conversion-rate optimization is big business. The question is: Where should you start? 

By understanding your specific needs and available resources and setting a plan to reach your expectations. 

Large enterprises often have the luxury of investing heavily in tools and expertise to accelerate their results. But does your company operate on the same scale? Do you have the right team, tools, and expertise to deploy at the same speed? 

As we are now in the peak usage of on-demand services, cloud computing, and microservices, waiting two weeks for a release is not exactly ideal.  

We have seen customers expect seamless experiences, instant updates, and zero downtime. To meet these expectations, development cycles have shrunk from weeks to hours or even minutes. 

So, in these conditions how can you ensure quality without slowing down? How do you test features that interact with countless microservices? The answer lies in a paradigm shift: API-first testing. 

Why Working Hard Is Not Solving Anything: The problem with Traditional Testing Methods. 

The shift to microservices and cloud-native architectures is making traditional testing methods increasingly obsolete. Manual testing or spending countless hours on API- testing is no longer sustainable.  

These methods are slow, error-prone, and fail to address the problems faced in modern systems. Teams that are still relying on old practices are now finding themselves struggling to keep up with the pace of development, leading to delayed releases and compromised quality. 

If your team is still stuck in two-week sprints for API testing, it might be time to rethink your strategy. Agile teams working in two-week sprints or similar cycles often face the following challenges: 

  1. Late-stage defect discovery: Bugs are detected late in the cycle, leading to rushed fixes that disrupt development workflows. This results in unstable software and an increased risk of deployment failures.
  2. Heavy reliance on UI testing: UI testing, while necessary, is slow, brittle, and resource intensive. It depends on the availability of a stable UI and is often prone to breaking with minor UI changes. 
  3. Limited test automation: Many teams struggle with automating tests beyond unit testing, leading to longer regression cycles and increasing testing debt. 
  4. Siloed teams: Developers, testers, and DevOps teams operate in isolation, causing integration issues that are discovered too late, often in staging or even production. 
  5. Microservices complexity: Modern applications rely on numerous APIs, and traditional testing methodologies fail to validate interactions effectively, making it difficult to ensure end-to-end quality. 

A survey by Capgemini found that over 60% of Agile teams struggle with test automation related issues, and nearly 70% of organizations report delays caused due to inadequate testing strategies  

Additionally, modern testers need API knowledge to stay relevant. API-first development is reshaping testing responsibilities, requiring QA professionals to go beyond UI-based testing and validate business logic at the service layer. 

Before you start guessing, What is API-First Testing? 

API-first testing enables teams to shift testing left, a way to ensure business logic, integrations, and data flows are validated at the API layer—long before they reach the UI.

This proactive approach is built on three core principles: 

  1. Test early, test often: Start testing APIs as soon as development begins. API-first testing allows teams to validate functionality before a UI is even built, catching defects early and reducing costly rework. 
  2. Automate everything: API tests run faster and are more reliable than UI tests. They enable automation of functional, performance, and security tests, ensuring comprehensive validation across services. 
  3. Enable cross-team collaboration: By using API-first testing, developers, testers, and DevOps teams work together using a shared suite 

 

The Impact That API-First Testing can Create 

Here’s how API-first testing directly impacts business outcomes:

1. Speed to Market

Before: Feature releases took 2-4 weeks due to lengthy test cycles.

After API-First Testing: Companies like Shopify can now ship updates 5x faster.

2. Improved Test Coverage

Before: UI tests covered only 50-60% of use cases.

After: API tests extend coverage to 95%+ of functional and integration scenarios.

3. Reduced Costs

Manual UI tests cost 4-5x more to maintain than API tests.

API automation cuts testing costs by 60-70% over time (World Quality Report 2023).

4. Faster Bug Detection

Before: Bugs discovered late, causing costly rework.

After: 75% of defects caught at the API level before UI development begins.

5. Seamless CI/CD Integration

API tests run in parallel with builds, enabling instant validation in DevOps pipelines.

How to Implement API-First Testing in Your Agile Workflow 

Step 1: Define API Contracts Upfront 

Teams should create API specifications using OpenAPI, Swagger, or GraphQL schemas. This ensures developers, testers, and stakeholders align on functionality before coding begins. 

Step 2: Shift Testing Left with Automated API Tests 

Use API testing tools like qAPI, Postman or Cypress to write tests that validate: 

  1. Functional correctness (input/output validation) 
  2. Performance (latency, response times) 
  3. Security (authentication, data leaks) 
  4. Resilience (handling failures, timeouts) 
Step 3: Integrate API Tests into CI/CD Pipelines 
  1. Run API tests automatically with each commit. 
  2. Fail builds when critical API endpoints break. 
  3. Use mocking to test dependencies without needing fully deployed services. 
Step 4: Monitor APIs in Production 

Continuous monitoring with tools like New Relic, Datadog, or Prometheus ensures that real-world API failures are detected before customers experience issues. 

Real-World Examples:

How Did Stripe Accelerate Their Deployments 

Stripe, the global payment processing giant, once struggled with release delays due to fragmented testing. Their challenge? Ensuring payment APIs functioned seamlessly across thousands of integrations. 

By adopting API-first testing: 

  1. They automated 90% of their test coverage at the API level. 
  2. Reduced regression testing time from hours to minutes. 
  3. Enabled engineers to deploy to production hundreds of times per day without fear. 

The result? Faster releases, fewer production incidents, and greater customer confidence. 

Testers at Stripe also had to upgrade their skill sets to API-focused automation, making them critical contributors to the development lifecycle  

How Amazon and Facebook Scaled Their API Releases  

Amazon’s Deployment Strategy: “You Build It, You Run It” 

Amazon is known for deploying updates to production every 11.7 seconds on average. Their success comes from: 

  1. Decentralized Development: Small, autonomous teams own their services end-to-end. 
  2. Automated API Testing: Every change undergoes rigorous automated API testing before release. 
  3. Feature Flags: Teams deploy code continuously but release features incrementally, reducing risk. 
  4. Canary Releases: New changes are tested in production with a small subset of users before a full rollout. 
Facebook’s Continuous Deployment Approach  

Facebook pushes updates twice a day using: 

  1. Shadow Testing: APIs are tested in a real-world production environment before a live launch. 
  2. Automated Rollbacks: If an issue is detected, Facebook’s system automatically reverts to a stable version. 
  3. Extensive API Contract Testing: Ensuring backward compatibility prevents breaking user experience. 
How Netflix Scaled to Daily Releases 

Netflix serves over 230 million users worldwide, delivering high-quality video with millisecond response times. But did you know they once struggled with release cycles? 

Their legacy testing relied on UI automation, which slowed them down. By transitioning to API-first testing, Netflix: 

  1. Reduced deployment time from weeks to hours. 
  2. Implemented chaos testing to simulate real-world API failures. 
  3. Achieved 99.99% uptime with microservices-based API testing. 

This transformation has enabled Netflix to push updates daily, personalize, recommendations, and handle billions of API requests seamlessly. 

Expanding API-First Testing: Key Challenges and Solutions 

While API-first testing offers numerous benefits, it does come with its own set set of challenges:
  1. Managing Test Data: Ensuring that API test cases use realistic, reusable, and isolated test data.
    2. Solution: We suggest using API mocking tools like WireMock or Pact to simulate real-world interactions.
  2. Security Testing Complexity: APIs are prime targets for attackers.
    3. Solution: Start by automating security scans using OWASP ZAP and Burp Suite to detect vulnerabilities early.
  3. Integration with Legacy Systems: Not all organizations operate in a modern microservices environment.
    4. Solution: Begin by implementing API gateways to standardize interactions between legacy and modern systems.

Key Questions to Ask About Your Testing Strategy: 

  1. Are you spending days debugging UI failures instead of catching defects at the API layer?  
  2. Are your testers equipped with the right API skills to validate business logic before it reaches production? 
  3. Are you using contract testing to prevent breaking API changes? 
  4. Are you leveraging service virtualization to test APIs independently? 
  5. How often are API tests executed in CI/CD pipelines? 
  6. Is your team equipped with API security testing capabilities? 
What Did These Questions Reveal 

Answering these questions provides insight into your team’s API testing maturity. If debugging UI failures is consuming most of your time, it’s a sign that critical defects aren’t being caught early enough.  

A lack of API skills among testers clearly shows a training gap that will slow down adoption of an API-first approach. Moreover, contract testing or service virtualization may expose your APIs to breaking changes or dependencies that affects independent testing. Limited CI/CD execution signals missed automation opportunities, while insufficient security testing leaves your APIs vulnerable.  

By identifying these gaps, you can prioritize initiatives that strengthen your API testing strategy. 

The reality is clear: Agile teams can no longer rely on UI testing alone. As software ecosystems grow more complex, API-first testing is no longer an option—it’s a necessity. 

The Challenge for QA Leaders 

Modern QA engineers are no longer just UI testers—they are quality enablers across the entire application stack. To remain competitive, testers must adopt an API-first mindset, integrating API validation, performance testing, and security assessments into their workflow. 

This shift is about more than just learning new tools; it’s about a proactive approach to quality that ensures APIs are robust before they ever reach production. 

Are you ready to rethink your approach to testing? The future of Agile development demands an API-first mindset. Will your team lead the transformation—or be left behind? 

 

Key Takeaways: 
  1. API versioning challenges: Many API failures occur due to improper version management. Backward compatibility testing is crucial to avoid breaking integrations. 
  2. Service virtualization impact: Simulating API dependencies enables early testing, reducing bottlenecks when real services are unavailable. 
  3. Contract testing reduces risk: By verifying API interactions at the contract level, teams minimize the need for complex end-to-end tests and detect breaking changes sooner. 
  4. API security gaps: We always see teams focus on functionality and they overlook API-specific vulnerabilities like broken authentication, excessive data exposure, and insufficient logging. 
  5. Performance issues from caching: Inconsistent cache configurations between environments can cause unexpected failures, making it essential to validate caching mechanisms during testing. 

Final Thought: Will Your Team Be Left Behind?

“Every company is a software company. You have to start thinking and operating like a digital company.” 

Satya Nadella Tweet

The companies that embrace API-first testing today will be the ones leading the software revolution tomorrow. Will your Agile team be ahead of the curve—or playing catch-up? The choice is yours. 

Want to Learn More? 

Explore how qAPI enables fast, reliable API testing. Start your free trial today and take your Agile team from sprints to daily releases! 

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The Untold Story of API Testing: How Tester-Centric Tools are Changing the Game 

Picture this: An ambitious FinTech startup is racing to launch a groundbreaking mobile app that promises seamless digital banking. The team’s deadline is tight, and the pressure to deliver is immense. Just days before the launch, during UI validation, testers uncover a critical issue—users can’t complete transactions during peak hours. The core problem? API bottlenecks that were never tested under real-world scenarios. 

We are at a phase where digital products are at the heart of business growth, APIs are the unsung heroes—powering everything from microservices and payment gateways to personalized customer experiences. Yet, many organizations approach API testing with a traditional-focused lens, leaving critical gaps that only testers can address. 

Let’s take a closer look at why this is a growing challenge. 

The Developer-Centric Dilemma 

Most teams rely on tools like Postman, Insomnia and SwaggerHub for API development and basic validation. While these tools are powerful for developers, they often fall short when it comes to the broader, user-focused testing needed to ensure seamless experiences. Why? 

  1. Developers test APIs for functionality ensuring endpoints respond correctly for a specific set of use cases—but often miss validating real-world scenarios 

  2. Many testers use the same scripts developers use and therefore are not actually creating proper coverage of test cases –  

  3. Integration bugs, edge cases, and UI-level failures slip through the cracks. 

  4. Testing focuses on the API in isolation rather than its role in complex systems. 

For example– Imagine an API handling loan applications for a digital bank. While it might respond correctly to individual requests, what happens when 10,000 users apply simultaneously? Or if a user inputs incorrect data, will it properly reject the request or still process it? Or when one downstream API—like credit scoring—is slow? It can potentially result in a cascading effect, disrupting the entire loan application process and impacting user experience.  

These scenarios are often missed in developer-centric testing. 

The Stakes in BFSI 

Financial institutions often handle critical operations like loan disbursements, transaction processing, and fraud detection through APIs. Any failure in these APIs can lead to not just financial loss but also regulatory penalties and reputational damage. 

Although essential for all industries like BFSI, FinTech, e-Commerce, and Retail companies are especially advised to have a robust API testing in place.  

In early 2024, a big API security breach occurred due to a buggy API that led to unauthorized access of close to 650,000 sensitive messages. This major outbreak not only exposed Office 365 passwords but also enabled unauthorized individuals to extract a ton of confidential information.  

Another example that comes to my mind is of Equifax, a credit reporting agency, which suffered a massive data breach in 2017 due to vulnerabilities in its APIs. The breach exposed the personal data of 147 million people and resulted in a $700 million settlement, highlighting the critical need for robust API testing. 

Ever Increasing Need for Tester-Centric Tools 

Ever Increasing Need for Tester-Centric Tools

This is where tools like qAPI come into play. Unlike traditional API development platforms, qAPI is designed with testers in mind, focusing on the bigger picture of quality assurance. 

Here’s how a tester centric tool empowers testers: 

  1. End-to-End Coverage: Testers can validate entire workflows—like a payment cycle from card authentication to receipt generation—to ensure seamless user experiences. 

  2. Performance at Scale: Performance testing simulates real-world loads, identifying bottlenecks before users do. 

  3. Codeless Automation: With AI-driven automation, testers can create robust test cases without writing a single line of code, reducing test cycles by over 60%. 

  4. AI-Powered Testing: The rise of AI analyses results, creates assertions, suggests additional use cases, prioritizing critical vulnerabilities for faster fixes. 

Why API Testing is Critical from a Tester’s View: Seeing the Big Picture 

A payment gateway API for a global bank failed to validate edge cases like duplicate transactions during high traffic periods. This oversight led to customer disputes and operational delays. Post-incident, the bank adopted a holistic API testing strategy, preventing similar issues in the future. 

Testers bring a unique perspective to API testing. Unlike developers, they think beyond the API’s individual functionality, focusing on: 

  1. User Journeys: Ensuring APIs support seamless end-to-end workflows. 

  2. Edge Cases: Simulating scenarios like incomplete data or unexpected user actions. 

  3. Negative or Exception test cases: validating authentication and preventing incorrect information from being added to applications/systems. 

  4. System Integration: Validating how APIs interact with databases, UIs, and other internal or third-party systems. 

Why Testers Need Empowerment 

  1. Holistic Quality: Testers ensure APIs to align with business and user needs, not just technical requirements. 
  2. Workflow Validation: They simulate real-world processes to uncover hidden bugs. 
  3. Efficiency Gains: Tools like qAPI empower testers to focus on strategic testing rather than repetitive tasks. 
Our Success Stories: 

Our client built a payment gateway API, that passed all developer-run tests but failed during UAT when processing refunds. Testers using qAPI identified the issue—an edge case where negative amounts weren’t validated—preventing customer disputes. 

Another success story is about a digital lender offering instant loans which suffered delays during peak hours due to API timeouts. By switching to a tester-centric approach with qAPI, they uncovered and fixed performance bottlenecks, improving transaction success rates by 30%. 

The Future—AI-Driven API Testing 

AI is transforming API testing in profound ways. Here’s how: 

  1. Automated Test Building: API tests are automatically built by simply interacting with a website  

  2. Predictive Testing: AI identifies potential edge cases, reducing manual effort. 

  3. Self-Healing Tests: When APIs change, AI-driven tests adapt automatically, minimizing maintenance. 

  4. Intelligent Debugging: Machine learning pinpoints root causes of failures faster. 

To give you context to how AI is transforming API testing, a digital bank’s loan processing API faced performance issues when many requests were made at the same time. AI-driven testing facilitated building and executing a performance test that flagged discrepancies in response times, enabling optimizations that improved processing speed by 25%. 

There are many success stories around how AI has augmented API testing for an organization and speed up their release cycles. Another such example is of an insurer who used AI to simulate high-volume claims processing, uncovering scalability issues before a major storm. This proactive testing ensured seamless service during a critical period. 

API testing isn’t just a technical task—it’s a strategic imperative. Organizations that fail to evolve risk losing customers, revenue, and trust. But those who empower testers with tools that can deliver exceptional quality, building trust and loyalty. 

Traditional testing was often limited to tracking individual API calls, but API process testing validates entire workflows, ensuring that every step functions as expected. A payment API, for instance, doesn’t just process transactions; it interacts with fraud detection, banking systems, and notification services.  

Without a process testing metric in place, a critical failure—like an order being placed but payment failing—can go unnoticed. Enabling testers to create complex workflows without writing code, making end-to-end validation easier and more reliable. 

Impact that a good API testing framework can have 

  1. Early Bug Detection: Catch issues in the development phase, reduces last-minute fixes and delays. This “shift-left” approach significantly reduces debugging efforts later in development.  For example- A banking app can validate its loan application API’s logic (credit checks, eligibility criteria) long before integrating it into the frontend, avoiding last-minute surprises.

  2. Automated Testing Efficiency : Every time a developer pushes new code for a ride-sharing app, API tests instantly validate critical workflows (booking, payments, driver availability), ensuring that the latest changes don’t break existing functionality.  Automating repetitive tests saves time and accelerates development cycles.
     
  3. Enhanced Reliability : API testing tools can simulate high traffic loads, helping organizations understand how their APIs perform under stress. This ensures that the system can handle peak usage scenarios, preventing downtime or performance drop after deployment. 

  4. Data Integrity and Accuracy : It ensures that the data being sent and received by the API is accurate and consistent. This is particularly important for applications that rely on real-time data processing or financial transactions, where even small errors can have significant consequences. 

  5. Empowering Agile & DevOps Workflows : APIs are central to DevOps automation, providing instant feedback on application health. With API monitoring systems in place, a SaaS company can detect performance spikes before customers are impacted, this largely contributes to a higher uptime. 
  • 78% of high-performing DevOps teams have automated API testing in their pipeline. 

An API can always function perfectly under normal conditions but what happens under high load, is always a grey area. API process-performance testing helps teams to beyond verifying correctness; it ensures that APIs perform reliably under different traffic conditions. For instance, imagine a travel booking API that has over thousands of simultaneous flight searches during a holiday sale. If response times degrade or errors spike, users abandon their bookings, directly impacting revenue. 

Moving ahead 

As we reflect on the evolving landscape of API testing, it becomes clear that this practice is no longer a mere step in the software development lifecycle—it’s a foundational pillar of digital success. APIs are the invisible threads weaving together our interconnected digital world.  

Whether it’s a financial institution enabling seamless cross-border payments, an e-commerce platform ensuring glitch-free checkouts during peak sales, or a healthcare provider safeguarding sensitive patient data, robust API testing underpins every meaningful interaction in our modern lives. 

The stakes are high, and the need for a paradigm shift is urgent. Developer-centric testing tools, while valuable, are not enough to address the complex workflows and real-world scenarios that define today’s digital ecosystems. This is where tester-centric platforms like qAPI shine, empowering QA teams with the tools, automation, and AI-driven insights they need to test smarter, faster, and more comprehensively. 

The future of API testing is bright, driven by innovation and a commitment to quality. It is a future where AI not only predicts and prevents failures but also accelerates the discovery of insights that drive better user experiences. It is a future where every tester is equipped with the resources to become a strategic enabler of success, transforming testing from a reactive process into a proactive, value-adding force. 

So, as you navigate the challenges of your digital journey, remember: API testing is not just about finding bugs—it’s about ensuring the resilience, scalability, and reliability of the digital experiences you deliver. Let this blog serve as your call to action. Empower your teams. Invest in the right tools. Rewrite the narrative of API testing and be the architect of a future where quality knows no compromise. 

What steps will you take today to redefine the role of API testing in your organization?

The story is yours to write. 
FAQs

Frequently Asked Questions

API testing focuses on verifying that application programming interfaces (APIs) meet expected functionality, performance, and security standards. It’s important because APIs are the backbone of modern applications, enabling communication between different software systems. As flawed APIs can lead to system crashes, data breaches, and poor user experiences.

While UI testing focuses on the graphical interface that users interact with, whereas API testing targets the underlying system-to-system communication. API testing is faster, more reliable, and ensures core functionalities work independently of the UI.

* Lack of comprehensive test coverage.
* Insufficient real-world scenario testing.
* Difficulty in automating complex workflows.
* Limited resources and tools tailored for testers, not developers.

Popular tools include: qAPI: A tester-centric tool for end-to-end and performance testing with codeless automation and AI insights. Postman: Great for manual testing by developers. Smartbear SOAP UI & ReadyAPI Smartbear Swagger: Ideal for API documentation and testing.

To perform API testing, follow these steps: Define the Scope: Identify which APIs and functionalities to test. Prepare Test Cases: Include test scenarios for positive, negative, and edge cases. Select Tools: Use tools like qAPI, Postman, or Swagger for efficient testing. Automate Where Possible: Use automation to reduce repetitive tasks. Execute Tests: Validate requests and responses. Analyze Results: Focus on debugging and resolving issues.

Functional Testing: Validates the API’s functionality as per requirements. Performance Testing: Assesses how APIs perform under different loads. Security Testing: Ensures APIs are safe from threats. Regression Testing: Ensures new changes don’t break existing functionality. Integration Testing: Checks how APIs interact with other components. End-to-End Testing: Tests complete workflows and processes.

Data Handling: Managing dynamic and large datasets. Test Coverage: Ensuring all scenarios, including edge cases, are tested. Complex Workflows: Handling dependencies across multiple APIs. Automation Challenges: Setting up scripts for complex processes. Frequent Updates: APIs often change, breaking existing tests.

qAPI is designed for testers, offering features like: End-to-end workflow validation. Scalable performance testing. AI-driven insights for faster debugging. Codeless automation to simplify test case creation.

Testers bring a holistic view to API testing by focusing on: User journeys and edge cases. Integration across systems. Early bug detection to reduce downstream costs.

Automation allows for: Continuous testing during development. Faster identification of issues. Reduced manual effort for repetitive tasks.

AI-driven API testing uses machine learning to: Predict edge cases. Self-heal test scripts when APIs change. Provide actionable insights for debugging.

TSB Bank (2018): Over £100 million lost due to untested API integrations during a system migration. Equifax (2017): A data breach caused by API vulnerabilities led to a $700 million settlement. E-commerce Platform (Black Friday): A single API dependency failure resulted in a 20% revenue loss.

FinTech: Ensures secure and efficient transactions. E-commerce: Supports seamless payment and inventory systems. Healthcare: Protects sensitive patient data. Insurance: Validates claim processing workflows.

Functional Testing: Verifies that APIs meet specified requirements (e.g., correct responses). Performance Testing: Assesses how APIs perform under load or stress, ensuring scalability and reliability.

API testing integrates into DevOps pipelines for: Continuous Integration and Delivery (CI/CD). Seamlessly integrating in CI/CD pipeline. Shifting left and Early bug detection. Faster release cycles with reliable APIs.

qAPI enables testers to: Use pre-built templates for common API scenarios. Leverage AI to generate test cases. Reduce dependency on scripting expertise.

Focus on real-world user workflows. Test for edge cases and system integrations. Automate repetitive tasks for efficiency. Monitor API performance under varying loads.

Increased use of AI for smarter testing. Greater emphasis on tester-centric tools. Enhanced integration with CI/CD pipelines.

Invest in tester-centric tools like qAPI. Empower QA teams with automation. Prioritize end-to-end and performance testing. Use AI to predict and prevent failures. Use AI to create test scripts without the knowledge of Javascript coding.

Tester-Centric Design: Built with QA teams in mind. Codeless Automation: Simplifies test creation. End-to-End Capabilities: Covers functional, performance, and process testing. AI-Powered Insights: Speeds up debugging and optimization. Scalable: Handles real-world traffic seamlessly.

Save upto 100+ Testing Hrs/Yr   With Our Guide to Codeless API Test Automation