Case StudyGame Testing

MMORPG Game Testing

Our client has a game product which is a free-to-play open-world survival RPG for mobile and PC.

Overview

The Game combines PvP and PvE modes as players fend off dual threats of the infected and other humans as they fight to survive in this apocalyptic wasteland.

Client Introduction

  • Client: A Chinese client
  • Domain: Survival RPG
  • Framework: Game application​
  • Development process: Agile

Business Needs

The client wanted us to:

  • Ensure that all game features work as expected
  • Ensure that the game fully supports different languages
  • Ensure that the game works well on Android and IOS operating systems and different types of mobile devices
Game Testing

Our Solution

Based on our client’s needs, LTS Group’s testing team did some tasks, including:

  • Functional testing
  • Client performance testing
  • Network testing
  • Compatibility & adaptation testing

Tech Stack

  • Android
  • iOS
  • Unreal engine
  • TAPD

Achievements

We created 15840 test cases and detected 244 bugs and did 20 regression runs​.

FinanceMobile AppMobile App

European Client – Cryptocurrency Wallet Development

Our client from Europe wanted to build a wallet for investing and storing digital assets.

Client Introduction

  • Client: A European client
  • Domain: Banking & finance

Business Challenge

The client needed a team to develop a wallet to store different digital assets, such as bitcoin, ethereum.

Our Solution

Based on our client’s needs, LTS Group’s development team successfully built the cryptocurrency wallet with key functionalities as follows:

  • Storing bitcoin, ethereum, etc.
  • Exchange and transfer assets.
  • Secure with 2FA, fingerprint.
Cryptocurrency Wallet Development for European Client

Achievements

In only 4 months, our team of 3 members successfully delivered the final wallet with 7 features.

Automated TestingAutomated TestingAutomated TestingAutomated TestingCase StudyCase StudyCase StudyEcommerce

eCommerce Platform Selling IQOS Devices Testing

Our client has an eCommerce platform that sells a line of heated tobacco and electronic cigarettes (IQOS) devices in the Japanese domestic market & other countries. This platform allows customers to purchase, replace, and upgrade their products and find out how to use the products.

Client Introduction

  • Client: International
  • Domain: eCommerce

Business Challenge

Multiple promotions & marketing campaigns were planned and executed regularly. Thus, the client needed to conduct many different test cases to ensure the purchasing, transaction, and stock replenishment processes work properly during new product releases and when promotions were applied.

Many payment methods were also supported, including:

  • Pay later
  • Pay by link (QR Code)
  • Pay by credit card
  • Cash on delivery (COD)
Testing for E-Commerce Platform Selling IQOS Devices

Our Solution

Based on our client’s needs, LTS Group’s testing team carried out various tasks:

  • Products were managed by unique IDs through a complex CMS system
  • Promotions and campaigns were updated every week, which required configuration adjustment in the backend to display the correct purchase amount on the front-end.
  • Integration tests were executed daily
  • Sanity tests were executed bi-weekly

Achievements

We successfully executed 900 test cases and detected 95 bugs.

Automotive

ADAS & LiDAR Testing and Development for Client in EU

In this case study, let’s discover how LTS Group successfully assisted a European automotive client, specializing in scalable vehicle architecture, to deliver high-quality ADAS & LiDAR testing and development as a cost-effective alternative to their previous vendor.

Client Introduction

Our client is a Tier 2 European automotive company focused on developing revolutionary vehicle architecture with full scalability to support diverse drive systems and advanced technology solutions.

Business Challenge

In an effort to reduce operational costs, the client was searching for a reliable vendor that could deliver high-quality results at a more competitive price point. After several rounds of evaluation, LTS Group was selected to take over their ADAS & LiDAR testing and development.

Scope of Work

  • Braking system​
  • Steering​
  • Safety​ (ADAS)

​Typical Projects

  1. BSW, MCAL for Ambient Light system​

Team size: 4​

Duration: 1/2023 ~ 12/2023

  1. Manual Test for ADAS – UDS on CAN and ETH​

Team size: 10​

Duration: 2/2024 ~ 8/2024

  1. CICD for E-Park Lock, E-Shift Lock​

Team size: 5​

Duration: 2/2024 ~ 8/2024​

Technologies & Tools​

  • BSW, MCAL, SIL​
  • Autosar, ASPICE LV2, SHA-256, Vector vFLASH​
  • C, CAPL, Davinci Configurator & Developer, Config MCAL on EB Tresos​
  • vTestStudio, vCast, Matlab, Simulink, Helix QAC, Boot Loader.​
  • Jenkins, Gitlab, Docker​

Unit Testing vs Functional Testing: Navigating Key Differences

The difference between unit testing vs functional testing primarily lies in their nature and scope. Unit testing is a testing level focused on individual components, while functional testing is a type of testing that evaluates the system’s overall behavior.

Before comparing these approaches, it’s crucial to understand that unit testing and functional testing are not mutually exclusive. Unit testing is a subset of functional testing, and the unit testing phase includes functional testing activities at the component level. These methods are complementary and can run in parallel.

Both unit testing and functional testing aim to ensure software quality by validating functionalities and acting as early detectors of issues, preventing them from escalating.

Distinguishing unit testing vs functional testing is not about choosing which is better, but about gaining a more accurate, comprehensive view of the testing layers throughout the software development life cycle. The ultimate goal is to combine unit testing and functional testing to achieve the best possible testing outcomes.

Let’s focus on this goal and dive deeper into analyzing the nature, principles, and best practices of unit testing vs functional testing!

 

Unit Testing vs Functional Testing: Key Differences

Aspects Unit testing Functional testing
Definition Unit testing is a software testing level in which each individual unit or component of code is tested to validate its correctness. Functional testing is a software testing type that evaluates the entire software application to verify that each function operates according to requirement specifications.
Scope Tests individual units or components of code. Tests the functionality of the entire application.
Granularity Tests are small and isolated, focusing on a single function or module. Tests are broader, covering multiple functions/modules and their interactions.
Objective Validates the behavior of a specific unit, ensuring it works as expected in isolation. Validates end-to-end functionality from a user’s perspective, ensuring the application meets requirements.
Speed Faster execution as tests are smaller and targeted. Slower execution due to broader scope and more complex scenarios.
Execution method Can be manual or automated testing. Typically automated.
Feedback Immediate feedback on individual code changes, aiding in rapid development. Feedback on overall functionality and user experience, identifying potential issues before release.
Coverage Focuses on code coverage within a unit, ensuring all code paths are tested. Focuses on ensuring all features work as expected, covering various user interactions.
Stubs & Drivers Not typically used. Often require stubs and drivers for components not yet developed
Setup Requires minimal setup, often utilizing mocks or stubs to isolate units. Requires comprehensive setup to replicate real-world environments, including databases, servers, and configurations.
Maintenance Require updates when code changes occur, ensuring tests reflect current code behavior. May need updates as requirements change, ensuring tests align with updated functionality.
Debugging Helps in identifying and fixing bugs early in the development process. Helps in identifying integration and user interface issues, improving overall application stability.
When to run Generally executed after unit and integration tests. Frequently executed, usually after any modification in code.

 

Functional Testing Overview

What is functional testing?

Functional testing is a type of software application testing that examines the functionality of a software application or system. This process focuses on assessing if the software app functions as intended and meets the business expectations.

The purpose of functional testing is to validate the application’s features, capabilities, and interactions with different components. It is the process of testing the software app’s input and output, user interactions, data manipulation, and the system’s response to various scenarios and conditions.

Types of functional testing:

  • Unit test
  • Smoke test
  • Sanity test
  • Integration test
  • System test
  • Regression test
  • User acceptance test

Advantages & disadvantages 

Functional testing is a crucial aspect of every software development plan. Besides its obvious benefits, it shows some drawbacks as well. Here are some key advantages and disadvantages of functional tests that businesses should take into account:

Advantages Disadvantages
Ensures requirement adherence: Functional test is specifically designed to verify that the software’s functionality aligns with the user and business’s needs and requirements. This means that every future, input, and output is examined against the expectations set out in the requirements. Time-consuming: Implementing comprehensive functional test cases for all user requirements requires significant effort and time investment. This involves writing, maintaining, and executing detailed functional tests.
User-focused validation: Functional test focuses on ensuring the final product is user-friendly and meets customer satisfaction. This type of test is created based on user stories, use cases, and specific functionalities that the users expect, ensuring that the software does what the users need it to do. Incomplete coverage: Functional testing may not cover all possible functional scenarios, resulting in missed defects and bugs. This is because it focuses on testing specific functionalities as defined by requirements, which might not include all edges.
Early defect detection: This is a significant benefit of functional testing because it allows for the identification and resolution of the software’s defects from the beginning. This helps reduce the development cost and effort required to fix them later. Resource intensive: Functional testing can require significant resources, including time, personnel, and tools, to perform thoroughly. This can be especially challenging for smaller teams with limited resources.
Supports automation test: Functional tests can be automated, increasing testing efficiency, and allowing for frequent and consistent testing throughout development. Maintenance overhead: Maintaining functional test cases can be burdensome, especially in agile environments with frequent changes. Test cases must be updated regularly to reflect changes in the requirements and functionalities.
Facilitates regression testing: Functional tests are useful for regression testing. It helps ensure that new changes or updates do not adversely affect the existing functionalities. Dependency on clear requirements: The effectiveness of functional testing depends heavily on the availability and clarity of requirements. Ambiguous or incomplete requirements can lead to ineffective testing and missed defects.

 

When to perform functional testing?

Here are signs indicating when businesses should use functional testing:

  • When preparing to release new software versions or updates: Functional test ensures that new features work as intended and existing functionalities remain unaffected, maintaining the software’s reliability and user satisfaction.
  • Whenever there are changes to the software’s requirements or specifications: Functional testing helps validate that the software still meets the updated criteria, preventing regression issues and ensuring alignment with stakeholder expectations.
  • Before integrating third-party systems or components into the software: Functional testing verifies that the integration works correctly and does not disrupt the overall functionality of the system, ensuring smooth interactions between different parts of the software ecosystem.
  • When implementing changes or enhancements to the user interface (UI): Functional testing ensures that the UI changes are implemented correctly and do not introduce usability issues or interfere with user interactions, maintaining a positive user experience.
  • After fixing defects or bugs identified during previous testing phases: Functional testing validates that the fixes are effective and do not introduce new issues, ensuring that the software remains stable and reliable after bug resolution.
  • During the final stages of software development before deployment: Functional testing serves as a comprehensive validation of the entire system, from end to end, ensuring that all functionalities work together seamlessly and meet the intended requirements before release.

Functional testing is indeed essential for the above situations. It is uniquely suited to address the above signs thanks to its focus on validating the software’s functional behavior and adherence to requirements.

 

Popular frameworks and tools in functional tests

Here’s a list of top functional testing tools and frameworks that the QA team can use:

  • Selenium: This is one of the most popular open-source automation testing frameworks for web applications. It supports multiple programming languages such as Java, Python, C#, and more.
  • Cypress: This modern JavaScript-based testing framework is specifically designed for web apps. It offers an all-in-one testing solution with features like a built-in assertion library, real-time browser testing, and easy setup.
  • Playwright: It is an open-source automation tool developed by Microsoft, used for testing web apps across different browsers (Chrome, Firefox, Chromium, Microsoft Edge, and WebKit).
  • Appium: This is an open-source automation tool used for testing mobile applications on various platforms such as iOS, Android, and Windows.
  • Soap UI: SoapUI is a widely used API testing tool for testing SOAP and RESTful web services. It allows testers to create, execute, and automate tests for web services, including functional testing, performance testing, and security testing.
  • WatirC:  Watir (Web Application Testing in Ruby) is an open-source automation framework for web applications, developed using the Ruby programming language. It supports cross-browser testing and integrates with various testing tools and libraries.

Depending on the specific needs of the project and the preferences of the testing team, testers can choose the most suitable framework or tool to ensure effective functional testing.

 

Unit Testing Overview

What is unit testing?

Unit testing is one of the software application testing levels. It focuses on testing individual units or components of the software in isolation. 

This functional testing type verifies the correctness of each unit’s behavior and functionality, ensuring that each part of the software works as intended.

This process is most useful during development to detect and fix defects early in the coding phase. By writing code in small, functional units and creating a corresponding unit test for each one, developers can maintain high code quality.

These unit tests are written as code and run automatically whenever changes are made to the software. If a test fails, it quickly identifies the specific area of the code with a bug or error, facilitating faster debugging and more efficient development.

 

What is a unit test?

A unit test is a block of code designed to validate the accuracy of a smaller, isolated block of application code, usually a function or method. Its goal is to ensure that the block of code performs as expected, based on the developer’s intended logic.

A single block of code may also have multiple unit tests, known as test cases. A complete set of test cases covers the full expected behavior of the code block.

 

Advantages & disadvantages

Since unit testing is a type of functional testing, it possesses all of the functional testing pros and cons like early bug detection, improved code quality, yet time-consuming, and incomplete test coverage.

Besides that, here are some distinct advantages and disadvantages of unit tests that businesses should take into account:

Advantages Disadvantages
Unit tests are fast: We only need to build a small unit to test it, and the tests also tend to be rather small. In fact, one-tenth of a second is considered slow for unit tests. Setup complexity: The initial setup of unit testing frameworks, including configuring mocks and dependencies, can be intricate and time-consuming.
Unit tests are reliable: Simple systems and small units in general tend to suffer much less from flakiness. Furthermore, best practices for unit testing – in particular practices related to hermetic tests – will remove flakiness entirely. Integration challenges: Integrating individually tested units into the broader system architecture may present complexities due to dependencies and environmental disparities.
Unit tests isolate failures: Even if a product contains millions of lines of code if a unit test fails, you only need to search that small unit under test to find the bug. Potential test redundancy: Overlapping test coverage among unit tests can lead to redundancy, complicating maintenance and potentially obscuring genuine defects.

 

When to perform unit testing?

Here are suitable situations when unit testing proves more beneficial than other testing methods:

  • During the early stages of development: It is essential to implement unit tests at the beginning of the development life cycle. Unit testing allows for identifying and addressing defects at the individual component level, reducing the likelihood of defects propagating to higher levels and minimizing rework later in the development process.
  • When code changes are made frequently: Unit testing provides rapid feedback on the impact of these changes, helping developers catch regressions early and maintain code integrity throughout the development lifecycle.
  • When testing complex or critical components of the software: Unit testing ensures that each software’s component behaves as expected in isolation, allowing for thorough validation and reducing the risk of errors in critical functionality.
  • When continuous integration/delivery (CI/CD) is required: Integrating unit tests into CI/CD pipelines automates the testing process, enabling developers to catch and fix issues early in the development cycle and ensuring that only high-quality code is deployed to production. While other types of testing may also be integrated into CI/CD pipelines, unit tests are essential for validating individual units and detecting regressions quickly.

Popular frameworks and tools in unit tests

Here’s a list of top unit testing tools and frameworks that the QA team can use:

  • JUnit: JUnit is one of the most widely used unit testing frameworks for Java applications. It provides annotations and assertions to write and execute tests easily.
  • Jasmine: Jasmine is a behavior-driven development (BDD) framework for testing JavaScript code. It provides an easy-to-understand syntax for defining tests and assertions, making it suitable for front-end and back-end testing in JavaScript environments.
  • TestNG: TestNG is a Java testing framework, inspired by JUnit and NUnit. It offers additional features beyond JUnit, such as support for parameterized tests, test grouping, and parallel test execution, making it a popular choice for Java developers.
  • PHPUnit: PHPUnit is a unit-testing framework for PHP applications. It offers a comprehensive set of assertion methods and features for testing PHP code.
  • Mocha: Mocha is a flexible and feature-rich JavaScript testing framework for Node.js applications. It supports asynchronous testing and various reporting formats.

 

Differentiating Factors of Functional Testing vs Unit Testing

The differences between unit vs functional testing are fundamental, lying in their testing scope and levels. 

Unit Testing is concentrated on individual units or components of code, ensuring that each part functions correctly in isolation.

On the other hand, functional testing focuses on verifying the overall functionality of the software application, ensuring it meets specified business requirements and user expectations. Functional tests encompass broader scenarios, validating end-to-end functionality from the user’s perspective to ensure the software behaves as intended in a real-world environment.

 

Key Similarities of Unit Testing and Functional Testing

Despite their distinct approaches, unit testing and functional testing are akin to companions with mutual objectives. Both techniques prioritize ensuring the software’s utmost reliability and excellence. They function as early detectors, identifying issues before they escalate.

Additionally, unit testing and functional testing share some similar advantages and disadvantages. The primary advantage of both is the early detection of defects, which helps reduce the cost and effort required to fix issues later in the development cycle. Both also contribute to higher code quality and reliability, providing confidence in the software’s stability.

However, they share the disadvantage of requiring significant initial effort to write comprehensive test cases, which can be time-consuming. Maintenance of these tests can also be challenging, especially when the codebase evolves, necessitating updates to the tests to ensure they remain relevant and effective.

Despite these challenges, the benefits they offer in terms of ensuring robust, high-quality software make both unit testing and functional testing indispensable in modern software development practices.

 

Keep Balance of both Functional Testing and Unit Testing

There is no absolute balance between functional vs unit testing. Because the testing implementers do not need to choose between these two types of tests; QA experts can and should perform them in parallel. As clarified at the beginning of the article, unit testing vs functional testing are not mutually exclusive but rather complementary and often overlap.

Unit tests typically target specific features at the module or class level, whereas functional tests evaluate use-case scenarios from the user interface to the end of processing.

For example, in an e-commerce web app, a critical function is product searching, which includes searching across all categories and using custom filters. During unit testing, developers, who have access to the backend code (a white-box testing technique), test individual modules: Developer A might test the search-all-categories module, while Developer B tests the search-by-custom-filter module. This is unit testing.

When developers examine functionalities such as searching, choosing filters, and sorting results, they are conducting functional testing at a unit level. However, the functional test covers broader testing, including testing levels other than unit tests, which are integration tests and system tests (end-to-end testing). 

Continuing with the e-commerce example, integration testing verifies that the search-all-categories module and the search-by-custom-filter module work well together. System testing evaluates the entire workflow, from account creation, logging in, searching for products, and adding products to the cart, to payment, ensuring a seamless and logical user flow.

To achieve comprehensive testing coverage, from individual components to the system as a whole, and ensure flawless testing outcomes, businesses should combine not only unit testing vs functional testing but these 3 levels of functional tests. This aligns closely with Agile developments, enhancing software quality while maintaining adaptability and speed.

Relying exclusively on functional testing without a well-balanced stratification strategy, or over-relying solely on unit testing, can lead to significant consequences. These include missing critical defects, difficulties in debugging, longer feedback loops, and higher costs for fixing bugs. 

Therefore, the optimal balance is the strategic use of functional testing at different levels, including unit testing, to ensure thorough and effective testing outcomes.

Here are some best practices to navigate unit testing vs functional testing and other test levels of functional test:

 

Stick to the testing objectives 

Every software development project has three fundamental objectives: correctness of features, clean and maintainable code, and a productive workflow:

  • Correctness of features:  Ensures that the software meets both functional and non-functional requirements, delivering the intended outcomes accurately and reliably.
  • Clean and maintainable code: Aims to create a codebase that is readable, flexible, and scalable, reducing technical debt and ensuring long-term sustainability.
  • Productive workflow: Focuses on fostering effective team dynamics, enhancing efficiency, and shortening development cycles to streamline processes and increase the likelihood of project success.

 

Understand all the testing types and levels thoroughly

Having a comprehensive understanding of each testing type and level is crucial for the QA team to plan effective testing plans and utilize each approach’s strengths at the appropriate time.

Let’s find out the most fundamental knowledge of each functional testing level in the following table:

Aspects Unit tests Integration tests End-to-end tests (system tests)
Focus Single functionality and small units of code Interaction between different modules or external systems The entire application as end users would experience it
Purpose Ensure new changes do not break existing functionality; maintain code quality Verify the system’s overall coherence and functionality at critical stages Validate the entire application’s workflow under real-world conditions
Number Numerous Fewer Fewest
Complexity Low Medium High
Scope Detailed and granular Moderate Broad
Execution time Quick Slower Longest
Frequency of execution Frequently, ideally part of automated continuous integration Less frequently than unit tests, at key points during the development cycle At significant milestones, such as before releases
Cost Low Medium – more expensive than unit tests Higher than both unit and integration tests
Examples Testing a single function or method Testing data flow between two modules Testing a user logging in and completing a transaction

 

To achieve the right balance among all testing types, the testing pyramid is an excellent method. The pyramid graphic below depicts the three essential layers of functional testing in a typical software development life cycle:

As shown, unit tests form the base of the testing pyramid, acting as the backbone of the software application testing process. As you move up the pyramid, the tests cover broader scopes and become more complex. Conducting frequent and thorough unit testing at the foundation level significantly reduces the risk of undetected bugs and issues.

Google recommends an ideal split of 70/20/10 for a balanced testing strategy: 70% unit tests, 20% integration tests, and 10% end-to-end functional tests. While these exact proportions may vary for different teams, the foundational pyramid structure serves as a consistent guide.

 

Understand the true value of tests 

The greatest value a software product team can offer end-users is not merely identifying bugs, but ensuring those bugs are resolved. To fix a bug, it must first be detected, ideally through a test designed to catch it.

However, value is only truly added when the bug is fixed. Therefore, when evaluating any testing strategy, it’s not enough to consider how well it identifies bugs. It’s equally important to assess how effectively it enables developers to fix and prevent bugs.

 

Build the right feedback loop

Tests establish a feedback loop that informs developers whether the product is functioning correctly. An ideal feedback loop is characterized by speed, reliability, and the ability to isolate failures.

  • Speed: Fast feedback allows for quicker fixes, and with a fast enough loop, developers can even run tests before committing changes.
  • Reliability: Reliable tests are crucial. Spending hours debugging only to find out it was a flaky test erodes developers’ trust in the test suite.
  • Isolation of failures: Finding the specific lines of code causing a bug in a product with millions of lines is akin to searching for a needle in a haystack. Isolating failures helps developers pinpoint and address issues effectively.

To create this ideal feedback loop, focus on smaller, more manageable components. Unit tests, which isolate and test small parts of the product, are particularly effective in creating an optimal feedback loop.

 

Getting solutions from experts

Effectively combining and optimizing different levels of functional testing requires precise professional knowledge of each test type and extensive functional testing experience.

Building and training an in-house QA team demands significant time, effort, and cost. As a result, many businesses opt to outsource to software testing companies for the following benefits:

  • Quick access to a vast pool of QA experts with extensive experience and professional knowledge in all types of functional testing.
  • Cost savings by reducing expenses on infrastructure, recruitment, and training.
  • Accelerated time to market by minimizing the time and effort required for hiring and training.
  • An optimized and flawless testing process, thanks to the expertise of the outsourced team.

Engaging a specialized software QA and testing firm enhances functional and unit testing, ensuring comprehensive evaluation and optimal testing performance.

With over 8 years of experience as a pioneering independent software QA provider in Vietnam, LQA stands out as a leading IT quality and security assurance organization. We offer a wide range of software QA and testing services, and software development solutions to meet our client’s diverse needs.

At LQA, we stay current with the latest industry-leading tools and functional testing methodologies.

Key features of LQA’s functional test solution:

  • Comprehensive software QA solutions: consultation, strategy, execution, and ongoing support.
  • A bug rate of less than 3% for devices, mobile, and web applications.
  • Quick delivery facilitated by a large team of experienced testers.
  • An optimal price-to-quality ratio, leveraging cost savings and the expertise of Vietnamese IT professionals.
  • Tailored solutions based on industry-specific knowledge.
  • Maximum security assured through a Non-Disclosure Agreement (NDA) and stringent security procedures during database access.

Connect with LQA’s professionals to enhance the functional testing experience, ensuring outstanding software quality, bug-free applications, quick project delivery, cost-effective solutions, industry-specific precision, and maximum security.

 

Frequently Asked Questions About Unit Tests and Functional Tests

What is unit testing?

Unit testing is a software testing level where individual units or components of a software application are tested in isolation. The purpose is to validate that each unit functions correctly as per the design specifications.

 

What is functional testing?

Functional testing is a type of software testing that evaluates the overall functionality of a software application by testing its features against specified requirements. It involves testing the application’s behavior and functionality from an end-user perspective. Major functional testing levels include: unit testing, integration testing, and system testing (end-to-end testing)

 

What is the difference between unit test and functional test?

The main difference between unit testing vs functional testing lies in their scope and focus. Unit testing is focused on testing individual components or units of the software application in isolation, typically at the code level. On the other hand, functional testing evaluates the behavior and functionality of the application as a whole, typically from the end user’s perspective, and encompasses testing various features and functionalities of the application.

 

Final Thoughts About Functional Tests vs Unit tests

In conclusion, unit testing and functional testing are not mutually exclusive but rather complementary. While unit testing focuses on individual components, functional testing evaluates the system’s overall behavior. Achieving an optimal balance between unit test vs functional test methods involves performing them in parallel to ensure thorough and effective testing outcomes.

Our article has served as a comprehensive guide to understanding the differences and nuances between unit tests vs functional tests.

Effectively combining and optimizing different levels of testing, including functional test vs unit test requires precise professional knowledge and extensive experience in functional testing.

Engaging a specialized software QA and testing firm, such as LQA, can enhance unit testing vs functional testing, ensuring comprehensive evaluation and optimal testing performance. For further assistance and to enhance your testing experience, we encourage you to connect with LQA’s experts.

Mobile App

How to Perform Native App Testing: A Complete Walkthrough

Native apps are known for their high performance, seamless integration with device features, and superior user experience compared to hybrid or web apps. But even the most well-designed native app can fail if it isn’t thoroughly tested. Bugs, compatibility issues, or performance lags can lead to poor reviews and user drop-off.

In this article, we’ll walk businesses through the purpose and methodologies of native app testing, explore different types of tests, and outline the key criteria to look for in a trusted native app testing partner.

By the end, companies will gain the insights needed to manage external testing teams with confidence and drive better app outcomes.

Now, let’s start!

What Is Native App Testing?

Native app testing is the process of evaluating the functionality, performance, and user experience of an app that is specifically developed for a particular operating system, such as iOS or Android, to make sure it functions correctly and delivers a high-quality user experience on its intended platforms. These apps are referred to as “native” because they are designed to take full advantage of the features and capabilities of a specific OS.

what is native app testing

Definition of native app testing

The purpose of native app testing is to determine whether native applications work correctly on the platform for which they are intended, evaluating their functionality, performance, usability, and security.

Through robust testing, this minimizes the risk of critical issues and enhances the app’s success in a competitive app market.

Key Types of Native App Testing

types of native app testing

Key types of native app testing

Unit testing

  • Purpose: Verifies that individual functions or components of the application work correctly in isolation.
  • Why it matters: Detecting and fixing issues at the unit level helps reduce downstream bugs and improves code stability early in the development cycle.

Integration testing

  • Purpose: Checks how different modules of the app work together – like APIs, databases, and front-end components.
  •  Why it matters: It helps identify communication issues between components, preventing system failures that can disrupt core user flows.

UI/UX testing

  • Purpose: Evaluates how the app looks and feels to users – layouts, buttons, animations, and screen responsiveness.
  • Why it matters: A consistent and intuitive interface enhances user satisfaction and directly impacts adoption and retention rates.

Performance testing

  • Purpose: Tests speed, responsiveness, and stability under different network conditions and device loads.
  • Why it matters: Ensuring smooth performance minimizes app crashes and load delays, both of which are key factors in maintaining user engagement.

Security testing

  • Purpose: Assesses how well the app protects sensitive data and resists unauthorized access or breaches.
  • Why it matters: Addressing security gaps is essential to protect sensitive information, meet compliance requirements, and maintain user trust.

Usability testing

  • Purpose: Gathers real feedback from users to identify friction points, confusing flows, or overlooked design flaws.
  • Why it matters: Feedback from usability testing guides design improvements and ensures that the app aligns with user expectations and behaviors.

Learn more: Software application testing: Different types & how to do?

Choosing the Right Approach to Native App Testing: In-House, Outsourced, or Hybrid?

One of the most strategic decisions in native app development is determining how testing will be handled. The approach taken can significantly affect not only time-to-market but also product quality, development efficiency, and long-term scalability.

approach to native app testing

Choose the right approach to native app testing

In-house testing

In-house testing involves building a dedicated QA team within the organization. This approach offers deep integration between testers and developers, fostering immediate feedback loops and domain knowledge retention.

Maintaining in-house teams makes sense for enterprises or tech-first startups planning frequent updates and long-term support.

Best fit for:

  • Companies developing complex or security-sensitive apps (e.g., fintech, healthcare) require strict control over IP and data.
  • Organizations with established development and QA teams capable of building and maintaining internal infrastructure.
  • Long-term products with frequent feature updates and the need for cross-functional collaboration between teams.

in-house testing

In-house testing

Challenges:

  • High cost of QA talent acquisition and retention, particularly for senior test engineers with mobile expertise.
  • Requires significant upfront investment in devices, testing labs, and automation tools.
  • May face resource bottlenecks during high-demand development cycles unless teams are over-provisioned.

Outsourced testing

With outsourced testing, businesses partner with QA vendors to handle testing either partially or entirely.

This model not only reduces operational burden but also gives businesses quick access to experienced testers, broad device coverage, and advanced tools. In fact, 57% of executives cite cost reduction as the primary reason for outsourcing, particularly through staff augmentation for routine IT tasks.

Best fit for:

  • Startups or SMEs lacking internal QA resources are seeking cost-effective access to mobile testing expertise.
  • Projects that require short-term testing capacity or access to specialized skills like performance testing, accessibility, or localization.
  • Businesses looking to accelerate time-to-market without sacrificing testing depth.

Challenges:

  • Reduced visibility and control over daily test execution and issue resolution timelines.
  • Coordination challenges due to time zone or cultural differences (especially in offshore models).
  • Requires due diligence to ensure vendor quality, security compliance, and confidentiality (e.g., NDAs, secure environments).

outsourced testing

Outsourced testing

Hybrid model

The hybrid approach for testing allows companies to retain strategic oversight while extending QA capabilities through external partners. In this setup, internal QA handles core feature testing and critical flows, while external teams take care of regression, performance, or multi-device testing.

Best fit for:

  • Organizations that want to retain strategic control over core testing (e.g., test design, critical modules) while outsourcing repetitive or specialized tasks.
  • Apps with variable testing workloads, such as cyclical releases or seasonal feature spikes.
  • Companies scaling up who need to balance cost and flexibility without compromising on quality.

Challenges:

  • Needs strong project management and alignment mechanisms to coordinate internal and external teams.
  • Risk of inconsistent quality standards unless test plans, tools, and reporting are well integrated.
  • May involve longer onboarding to align both sides on tools, workflows, and business logic.

hybrid model

Hybrid model

5 Must-Have Criteria for a Trusted Native App Testing Partner

While every business has its own unique needs, there are key qualities that any reliable native app testing partner should consistently deliver. Below, we break down the 5 essential criteria that an effective software testing partner must meet and explain why they matter.

trusted native app testing partner

Choose a trusted native app testing partner

Proven experience in native app testing

A testing partner’s experience should extend beyond general QA into deep, hands-on expertise in native mobile environments. Native app testing demands unique familiarity with OS-level APIs, device hardware integration, and platform-specific performance constraints, whether it’s iOS/Android for mobile or Windows/macOS for desktop.

  • For mobile, this means understanding how apps behave under different OS versions, permission models, battery usage constraints, and device-specific behaviors (e.g., Samsung vs. Pixel).
  • For desktop, experience with native frameworks like Win32, Cocoa, or Swift is critical, especially for apps relying on GPU usage, file system access, or local caching.

Businesses should see case studies or proof points in your industry or use case, such as finance, healthcare, or e-commerce, where reliability, compliance, or UX is critical.

Certifications like ISTQB, ASTQB-Mobile, or Google Developer Certifications reinforce credibility, especially when combined with real-world results .

Robust infrastructure and real device access

A trusted testing partner must offer access to a wide range of real devices and system environments that reflect the business’s actual user base across both mobile and desktop platforms. This includes varying operating systems, screen sizes, hardware specs, and network conditions. Unlike limited simulations, testing on real devices ensures accurate performance insights and reduces post-launch issues.

Security, compliance, and confidentiality

Given the sensitive nature of app data, the native app testing partner must adhere to strict security standards and compliance frameworks (e.g., ISO 27001, SOC 2, GDPR).

More than just certification, this means implementing security-conscious testing environments that prevent data leaks, applying techniques like data masking or anonymization during production-like tests, and enforcing strict protocols such as signed NDAs, role-based access, and secure handling of test assets and code.

It’s also important to note that native desktop apps often interact more deeply with a system’s file structure or network stack than mobile apps do, which increases the surface area for security vulnerabilities.

Communication and collaboration practices

Clear, consistent communication is essential when working with an external testing partner. Businesses should expect regular updates on progress, test results, and issues so they can stay informed and make timely decisions. The partner should follow a structured process for planning, executing, and retesting and be responsive when priorities shift.

They also need to work smoothly within companies’ existing tools and workflows, whether that’s Jira for tracking or Slack for quick updates. Good collaboration helps avoid delays, improves visibility, and keeps your product moving forward efficiently.

Scalability and business alignment

An effective testing partner must offer the ability to scale resources in line with evolving product demands, whether ramping up for major releases or optimizing during low-activity phases. Flexible scaling guarantees efficient use of time and budget without compromising test coverage.

Equally important is the partner’s alignment with broader business objectives. Testing processes should reflect the development pace, release cadence, and quality benchmarks of the product. A well-aligned partner contributes not only to immediate project goals but also to long-term product success and market readiness.

Best Practices for Managing An External Native App Testing Team

For businesses exploring outsourced native app testing, effective team management is key to turning that investment into measurable outcomes. The 5 practices below help establish alignment, reduce friction, and unlock real value from the partnership.

manage an external native app testing team

Manage an external native app testing team

Define clear expectations from the start

A productive partnership begins with a clearly defined scope of work. Outline key performance indicators (KPIs), testing coverage objectives, timelines, and preferred communication channels from the outset.

Make sure the external testing team understands the product’s business goals, user profiles, and high-risk areas, whether it’s data sensitivity, user load, or platform-specific edge cases. Early alignment helps eliminate confusion, reduces the risk of missed expectations, and makes it easier to track progress against measurable outcomes.

Assign a dedicated point of contact

Appointing a liaison on both sides helps reduce miscommunication and speeds up decision-making. This role is responsible for managing test feedback loops, flagging blockers, and facilitating coordination across internal and external teams.

Integrate with development workflows

Embedding QA professionals within Agile teams enhances collaboration and accelerates issue resolution. When testers are involved from the outset, they can identify defects earlier, reducing costly rework and ensuring development stays on track.

In today’s multi-platform environment, where apps must perform reliably across operating systems, devices, and browsers, integrating QA into Agile sprints transforms compatibility testing into a continuous effort. Rather than treating it as a final-stage checklist, teams can proactively detect and resolve issues such as layout breaks on specific devices or OS-related performance lags.

Maintain consistent communication and reporting

Regular updates between the internal team and the external testing partner help avoid misunderstandings and keep projects on track. Weekly syncs or sprint reviews ensure that testing progress, bug status, and priorities are clearly understood.

Use structured reports and dashboards to show key metrics like test coverage, defect severity, and retesting status. As a result, businesses get to assess product quality quickly without wading through technical detail.

Connecting the external team to tools already in use, such as Jira, Slack, or Microsoft Teams, helps keep communication smooth. Such integration improves collaboration and speeds up release cycles.

Foster a long-term partnership mindset

Onboard the external testing team with the same thoroughness as internal teams. Provide access to product documentation, user personas, and business goals. When testers understand the broader context, they can identify issues that impact user experience and business outcomes more effectively. This strategic partnership fosters a proactive approach to quality, leading to more robust and user-centric products.

Check out the comprehensive test plan template for the upcoming projects.

How Long Does It Take To Thoroughly Test A Native App?

Thoroughly testing a native mobile application is a multifaceted endeavor. Timelines vary significantly based on:

  • App complexity (simple MVP vs. feature-rich platform)
  • Platforms supported (iOS, Android, or both)
  • Manual vs. automation mix
  • Number of devices and testing cycles

how long does it take to test a native app

How long does it take to test a native app?

For a basic native app, such as a content viewer or utility tool with limited interactivity, end-to-end testing might take between 1 and 2 weeks, focusing primarily on functionality, UI, and device compatibility.

However, most business-grade applications – those involving user authentication, server integration, data input/output, or performance-sensitive features – typically require from 3 to 6 weeks of testing effort.

For feature-rich or enterprise-level native apps, particularly those that involve real-time updates, background processes, or complex data transactions, testing can stretch from 6 to 10 weeks or more.

This is especially true when multi-platform coverage (iOS, Android, desktop) and a wide range of devices and OS versions are required. Native apps on mobile often need to account for fragmented hardware ecosystems, while native desktop apps may require deeper testing of system-level access, file handling, or offline modes.

Ultimately, the real question is not just “how long,” but how early and how strategically QA is integrated. Investing upfront in test strategy, automation, and risk-based prioritization often results in faster releases and lower post-launch costs, making the testing timeline not just a cost center but a business enabler.

FAQs About Native App Testing

  1. What is native app testing, and how is it different from web or hybrid testing?

Native app testing focuses on apps built specifically for a platform (iOS, Android, Windows) using platform-native code. These apps interact more directly with device hardware and OS features, so testing must cover areas like performance, battery usage, offline behavior, and hardware integration. In contrast, web and hybrid apps run through browsers or webviews and don’t require the same depth of device-level testing.

  1. How do I know if outsourcing native app testing is right for my business?

Outsourcing is a good choice when internal QA resources are limited or when there’s a need for broader device coverage, faster turnaround, or specialized skills like security or localization testing. It helps reduce time-to-market while controlling costs, especially during scaling or high-volume release cycles.

  1. How much does it cost to outsource native app testing?

While specific figures for outsourcing native app testing are not universally standardized, industry insights suggest that software testing expenses typically account for 15% to 25% of the total project budget. For instance, if the total budget for developing a native app is estimated at $100,000, the testing phase could reasonably account for $15,000 to $25,000 of that budget. This range encompasses various testing activities, including functional, performance, security, and compatibility testing.

Final Thoughts on Native App Testing 

By understanding what native app testing entails, weighing the pros and cons of different approaches, and applying best practices when working with external testing teams, businesses can make smart decisions. More importantly, companies will be better equipped to decide if outsourcing is the right path and how to do it in a way that maximizes efficiency.

Ready to get started? 

LQA’s professionals are standing by to help make application testing a snap, with the know-how businesses can rely on to go from ideation to app store.

With a team of experts and proven software testing services, we help you accelerate delivery, ensure quality, and get more value from your testing efforts.

Contact us today to get the ball rolling!

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How to Use AI in Software Testing: A Complete Guide

Did you know that 40% of testers are now using ChatGPT for test automation, and 39% of testing teams have reported efficiency gains through reduced manual effort and faster execution? These figures highlight the growing adoption of AI in software testing and its proven ability to improve productivity.

As businesses strive to accelerate development cycles while maintaining software quality, the demand for more efficient testing methods has risen substantially. This is where AI-driven testing tools come into play thanks to their capability to automate repetitive tasks, detect defects early, and improve test accuracy.

In this article, we’ll dive into the role of AI in software testing at length, from its use cases and advancements from manual software testing to how businesses can effectively implement AI-powered solutions.

What is AI in Software Testing?

As software systems become more complex, traditional testing methods are struggling to keep pace. A McKinsey study on embedded software in the automotive industry revealed that software complexity has quadrupled over the past decade. This rapid growth makes it increasingly challenging for testing teams to maintain software stability while keeping up with tight development timelines.

What is AI in Software Testing

What is AI in Software Testing?

The adoption of artificial intelligence in software testing marks a significant shift in quality assurance. With the ability to utilize machine learning, natural language processing, and data analytics, AI-driven testing boosts precision, automates repetitive tasks, and even predicts defects before they escalate. Together, these innovations contribute to a more efficient and reliable testing process.

According to a survey by PractiTest, AI’s most notable benefits to software testing include improved test automation efficiency (45.6%) and the ability to generate realistic test data (34.7%). Additionally, AI is reshaping testing roles, with 23% of teams now overseeing AI-driven processes rather than executing manual tasks, while 27% report a reduced reliance on manual testing. However, AI’s ability to adapt to evolving software requirements (4.08%) and generate a broader range of test cases (18%) is still developing.

Benefits of AI in software testing

Benefits of AI in software testing

AI Software Testing vs Manual Software Testing

Traditional software testing follows a structured process known as the software testing life cycle (STLC), which comprises six main stages: requirement analysis, test planning, test case development, environment setup, test execution, and test cycle closure.

AI-powered testing operates within the same framework but introduces automation and intelligence to increase speed, accuracy, and efficiency. By integrating AI into the STLC, testing teams can achieve more precise results in less time. Here’s how AI transforms traditional STLC’s stages:

  • Requirement analysis: AI evaluates stakeholder requirements and recommends a comprehensive test strategy.
  • Test planning: AI creates a tailored test plan, focusing on areas with high-risk test cases and adapting to the organization’s unique needs.
  • Test case development: AI generates, customizes, and self-heals test scripts, also providing synthetic test data as needed.
  • Test cycle closure: AI assesses defects, forecasts trends, and automates the reporting process.

While AI brings significant advantages, manual testing remains irreplaceable in certain cases.

For a detailed look at the key differences between the two approaches, refer to the table below:

Aspect Manual testing AI testing
Speed and efficiency Time-consuming and needs significant human effort.

Best for exploratory, usability, and ad-hoc testing.

Executes thousands of tests in parallel, reducing redundancy and optimizing efficiency.

Learns and improves over time.

Accuracy and reliability Prone to human errors, inconsistencies, and fatigue. Provides consistent execution, eliminates human errors, and predicts defects using historical data.
Test coverage Limited by time and resources. Suitable for real-world scenario assessments that automated tools might miss. Expands test coverage significantly, identifying high-risk areas and executing thousands of test cases within minutes.
Cost and resource Requires skilled testers, leading to high long-term costs. Labor-intensive for large projects. Best for small-scale applications. Reduces long-term expenses by minimizing manual effort. AI-driven testing automation tools automate test creation and execution, running continuously.
Test maintenance Needs frequent updates and manual adjustments for every software change, increasing maintenance costs. Self-healing test scripts automatically adjust to evolving applications, reducing maintenance efforts.
Scalability Difficult to scale across multiple platforms, demanding additional testers for large projects. Easily scalable with cloud-based execution, supporting parallel tests across different devices and browsers. Ideal for large-scale enterprise applications.

Learn more: Automation testing vs. manual testing: Which is the cost-effective solution for your firm?

Use Cases of AI in Software Testing

According to the State of Software Quality Report 2024, test case generation is the most common AI application in both manual and automated testing, followed closely by test data generation.

Still, AI and ML can advance software testing in many other ways. Below are 5 key areas where these two technologies can make the biggest impact:

Use Cases of AI in Software Testing

Use Cases of AI in Software Testing

Automated test case generation

Just like how basic coding tasks that once required human effort can now be handled by AI, in software testing, AI-powered tools can generate test cases based on given requirements.

Traditionally, automation testers had to write test scripts manually using specific frameworks, which required both coding expertise and continuous maintenance. As the software evolved, outdated scripts often failed to recognize changes in source code, leading to inaccurate test results. This created a significant challenge for testers working in agile environments, where frequent updates and rapid iterations demand ongoing script modifications.

With generative AI in software testing, QA professionals can now provide simple language prompts to instruct the chatbot to create test scenarios tailored to specific requirements. AI algorithms will then analyze historical data, system behavior, and application interactions to produce comprehensive test cases.

Automated test data generation

In many cases, using real-world data for software testing is restricted due to compliance requirements and data privacy regulations. AI-driven synthetic test data generation addresses this challenge by creating realistic, customized datasets that mimic real-world conditions while maintaining data security.

AI can quickly generate test data tailored to an organization’s specific needs. For example, a global company may require test data reflecting different regions, including address formats, tax structures, and currency variations. By automating this process, AI not only eliminates the need for manual data creation but also boosts diversity in test scenarios.

Automated issue identification

AI-driven testing solutions use intricate algorithms and machine learning to detect, classify, and prioritize software defects autonomously. This accelerates issue identification and resolution, ultimately improving software quality through continuous improvement.

The process begins with AI analyzing multiple aspects of the software, such as behavior, performance metrics, and user interactions. By processing large volumes of data and recognizing historical patterns, AI can pinpoint anomalies or deviations from expected functionality. These insights help uncover potential defects that could compromise the software’s reliability.

One of AI’s major advantages is its ability to prioritize detected issues based on severity and impact. By categorizing problems into different levels of criticality, AI enables testing teams to focus on high-risk defects first. This strategic approach optimizes testing resources, reduces the likelihood of major failures in production, and enhances overall user satisfaction.

Continuous testing in DevOps and CI/CD

AI plays a vital role in streamlining testing within DevOps and continuous integration/ continuous deployment (CI/CD) environments.

Once AI is integrated with DevOps pipelines, testing becomes an ongoing process that is seamlessly triggered with each code change. This means every time a developer pushes new code, AI automatically initiates necessary tests. This process speeds up feedback loops, providing instant insights into the quality of new code and accelerating release cycles.

Generally, AI’s ability to automate test execution after each code update allows teams to release software updates more frequently and with greater confidence, improving time-to-market and product quality.

Test maintenance

Test maintenance, especially for web and user interface (UI) testing, can be a significant challenge. As web interfaces frequently change, test scripts often break when they can no longer locate elements due to code updates. This is particularly problematic when test scripts interact with web elements through locators (unique identifiers for buttons, links, images, etc.).

In traditional testing approaches, maintaining these test scripts can be time-consuming and resource-intensive. Artificial intelIigence brings a solution to this issue. When a test breaks due to a change in a web element’s locator, AI can automatically fetch the updated locator so that the test continues to run smoothly without requiring manual intervention.

If this process is automated, AI will considerably reduce the testing team’s maintenance workload and improve testing efficiency.

Visual testing

Visual testing has long been a challenge for software testers, especially when it comes to comparing how a user interface looks before and after a launch. Previously, human testers relied on their eyes to spot any visual differences. Yet, automation introduces complications – computers detect even the slightest pixel-level variations as visual bugs, even when these inconsistencies have no real impact on user experience.

AI-powered visual testing tools overcome these limitations by analyzing UI changes in context rather than rigidly comparing pixels. These tools can:

  • Intelligently ignore irrelevant changes: AI learns which UI elements frequently update and excludes them from unnecessary bug reports.
  • Maintain UI consistency across devices: AI compares images across multiple platforms and detects significant inconsistencies.
  • Adapt to dynamic elements: AI understands layout and visual adjustments, making sure they enhance rather than disrupt user experience.

Adopt AI in software testing with LQA

How to Use AI in Software Testing?

Intrigued to dive deeper to start integrating AI into your software testing processes? Find out below.

How to Use AI in Software Testing

How to Use AI in Software Testing

Step 1. Identify areas where AI can improve software testing

Before incorporating AI into testing processes, decision-makers must pinpoint the testing areas that stand to benefit the most.

Here are a few ideas to get started with:

  • Automated test case generation
  • Automated test data generation
  • Automated issue identification
  • Continuous testing in DevOps and CI/CD
  • Test maintenance
  • Visual testing

Once these areas are identified, set clear objectives and success metrics for AI adoption. There are some common goals like increasing test coverage, test execution speed, and defect detection rates

Step 2. Choose between building from scratch or using proprietary AI tools

The next step is to choose whether to develop a custom AI solution or adopt a ready-made AI-powered testing tool.

The right choice depends on the organization’s resources, long-term strategy, and testing requirements.

Here’s a quick look at these 2 methods:

Build a custom AI system vs use proprietary AI tools

Build a custom AI system or use proprietary AI tools?

Build a custom AI system

In-house development allows for a personalized AI solution that meets specific business needs. However, this approach requires significant investment and expertise:

  • High upfront costs: Needs a team of skilled AI engineers and data scientists.
  • Longer development cycle: Takes more time to build compared to off-the-shelf AI tools.
  • Ongoing maintenance: AI models need regular updates and retraining.

Case study: NVIDIA’s Hephaestus (HEPH)

The DriveOS team at NVIDIA developed Hephaestus, an internal generative AI framework to automate test generation. HEPH simplifies the design and implementation of integration and unit tests by using large language models for input analysis and code generation. This greatly reduces the time spent on creating test cases while boosting efficiency through context-aware testing.

How does HEPH work? 

HEPH takes in software requirements, software architecture documents (SWADs), interface control documents (ICDs), and test examples to generate test specifications and implementations for the given requirements.

HEPH technical architecture

HEPH technical architecture

The test generation workflow includes the following steps:

  • Data preparation: Input documents such as SWADs and ICDs are indexed and stored in an embedding database, which is then used to query relevant information.
  • Requirements extraction: Requirement details are retrieved from the requirement storage system (e.g., Jama). If the input requirements lack sufficient information for test generation, HEPH automatically connects to the storage service, locates the missing details, and downloads them.
  • Data traceability: HEPH searches the embedding database to establish traceability between the input requirements and relevant SWAD and ICD fragments. This step creates a mapped connection between the requirements and corresponding software architecture components.
  • Test specification generation: Using the verification steps from the requirements and the identified SWAD and ICD fragments, HEPH generates both positive and negative test specifications, delivering complete coverage of all aspects of the requirement.
  • Test implementation generation: Using the ICD fragments and the generated test specifications, HEPH creates executable tests in C/C++.
  • Test execution: The generated tests are compiled and executed, with coverage data collected. The HEPH agent then analyzes test results and produces additional tests to cover any missing cases.

Use proprietary AI tools

Rather than crafting a custom AI solution, many organizations opt for off-the-shelf AI automation tools, which come with pre-built capabilities like self-healing tests, AI-powered test generation, detailed reporting, visual and accessibility testing, LLM and chatbot testing, and automated test execution videos.

These tools prove to be beneficial in numerous aspects:

  • Quick implementation: No need to develop AI models from the ground up.
  • Lower maintenance: AI adapts automatically to application changes.
  • Smooth integration: Works with existing test frameworks out of the box.

Some of the best QA automation tools powered by AI available today are Selenium, Code Intelligence, Functionize, Testsigma, Katalon Studio, Applitools, TestCraft, Testim, Mabl, Watir, TestRigor, and ACCELQ.

Each tool specializes in different areas of software testing, from functional and regression testing to performance and usability assessments. To choose the right tool, businesses should evaluate:

  • Specific testing needs: Functional, performance, security, or accessibility testing.
  • Integration & compatibility: Whether the tool aligns with current test frameworks.
  • Scalability: Ability to handle growing testing demands.
  • Ease of use & maintenance: Learning curve, automation efficiency, and long-term viability.

Also read: Top 10 trusted automation testing tools for your business

Step 3. Measure performance and refine

If a business chooses to develop an in-house AI testing tool, it must then be integrated into the existing test infrastructure for smooth workflows. Once incorporated, the next step is to track performance to assess its effectiveness and identify areas for improvement.

Here are 7 key performance metrics to monitor:

  • Test execution coverage
  • Test execution rate
  • Defect density
  • Test failure rate
  • Defect leakage
  • Defect resolution time
  • Test efficiency

Learn more: Essential QA metrics with examples to navigate software success

Following that, companies need to use performance insights to refine their AI software testing tools or adjust their software testing strategies accordingly. Fine-tuning algorithms and reconfiguring workflows are some typical actions to take for optimal AI-driven testing results.

Adopt AI in software testing with LQA

Challenges of AI in Software Testing

Challenges of AI in software testing

Challenges of AI in software testing

  • Lack of quality data

AI models need large volumes of high-quality data to make accurate predictions and generate meaningful results.

But, in software testing, gathering sufficient and properly labeled data can be a huge challenge.

If the data used to train AI models is incomplete, inconsistent, or poorly structured, the AI tool may produce inaccurate results or fail to identify edge cases.

These data limitations can also hinder the AI’s ability to predict bugs effectively, resulting in missed defects or false positives.

The need for continuous data management and governance is crucial to make sure AI models can function at their full potential.

  • Lack of transparency

One of the key challenges with advanced AI models, particularly deep learning systems, is their “black-box” nature. 

These models often do not provide clear explanations about how they arrive at specific conclusions or decisions. For example, testers may find it difficult to understand why an AI model flags a particular bug, prioritizes certain test cases, or chooses a specific path in test execution.

This lack of transparency can create trust issues among testing teams, who may hesitate to rely on AI-generated insights without clear explanations.

Plus, without transparency, it becomes difficult for teams to troubleshoot or fine-tune AI predictions, which may ultimately slow down the adoption of AI-driven testing.

  • Integration bottlenecks

Integrating AI-based testing tools with existing testing frameworks and workflows can be a complex and time-consuming process.

Many organizations already use well-established DevOps pipelines, CI/CD workflows, and manual testing protocols.

Introducing AI tools into these processes often requires significant customization for smooth interaction with legacy systems.

In some cases, AI tools for testing may need to be completely reconfigured to function within a company’s existing infrastructure. This can lead to delays in deployment and require extra resources, especially in large, established organizations where systems are deeply entrenched.

As a result, businesses must carefully evaluate the compatibility of AI tools with their existing processes to minimize friction and maximize efficiency.

  • Skill gaps

Another major challenge is the shortage of in-house expertise in AI and ML. Successful implementation of AI in testing software demands not only a basic understanding of AI principles but also advanced knowledge of data analysis, model training, and optimization.

Many traditional QA professionals may not have the skills necessary to configure, refine, or interpret AI models, making the integration of AI tools a steep learning curve for existing teams.

Companies may thus need to invest in training or hire specialists in AI and ML to bridge this skills gap.

Learn more: Develop an effective IT outsourcing strategy

  • Regulatory and compliance concerns

Industries such as finance, healthcare, and aviation are governed by stringent regulations that impose strict rules on data security, privacy, and the transparency of automated systems.

AI models, particularly those used in testing, must be configured to adhere to these industry-specific standards.

For example, AI tools used in healthcare software testing must comply with HIPAA regulations to protect sensitive patient data.

These regulatory concerns can complicate AI adoption, as businesses may need to have their AI tools meet compliance standards before they can be deployed for testing.

  • Ethical and bias concerns

AI models learn from historical data, which means they are vulnerable to biases present in that data.

If the data used to train AI models is skewed or unrepresentative, it can result in biased predictions or unfair test prioritization.

To mitigate these risks, it’s essential to regularly audit AI models and train them with diverse and representative data.

FAQs about AI in Software Testing

How is AI testing different from manual software testing?

AI testing outperforms manual testing in speed, accuracy, and scalability. While manual testing is time-consuming, prone to human errors, and limited in coverage, AI testing executes thousands of tests quickly with consistent results and broader coverage. AI testing also reduces long-term costs through automation, offering self-healing scripts that adapt to software changes. In contrast, manual testing requires frequent updates and more resources, making it less suitable for large-scale projects.

How is AI used in software testing?

AI is used in software testing to automate key processes such as test case generation, test data creation, and issue identification. It supports continuous testing in DevOps and CI/CD pipelines, delivering rapid feedback and smoother workflows. AI also helps maintain tests by automatically adapting to changes in the application and performs visual testing to detect UI inconsistencies. This leads to improved efficiency, faster execution, and higher accuracy in defect identification.

Will AI take over QA?

No, AI will not replace QA testers but will enhance their work. While AI can automate repetitive tasks, detect patterns, and even predict defects, software quality assurance goes beyond just running tests, it requires critical thinking, creativity, and contextual understanding, which are human strengths.

Ready to Take Software Testing to the Next Level with AI?

There is no doubt that AI has transformed software testing – from automated test cases and test data generation to continuous testing within DevOps and CI/CD pipelines.

Implementing AI in software testing starts with identifying key areas for improvement, then choosing between custom-built solutions or proprietary tools, and ends with continuously measuring performance against defined KPIs.

With that being said, successful software testing with AI isn’t without challenges. Issues like data quality, transparency, integration, and skill gaps can hinder progress. That’s why organizations must proactively address these obstacles for a smooth transition to AI-driven testing.

At LQA, our team of experienced testers combines well-established QA processes with innovative AI-infused capabilities. We use cutting-edge AI testing tools to seamlessly integrate intelligent automation into our systems, bringing unprecedented accuracy and operational efficiency.

Reach out to LQA today to empower your software testing strategy and drive quality to the next level.

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HealthcareWeb App

Healthcare Software Testing: Key Steps, Cost, Tips, and Trends

The surge in healthcare software adoption is redefining the medical field, with its momentum accelerating since 2020. According to McKinsey, telehealth services alone are now used 38 times more frequently than before the COVID-19 pandemic. This shift is further fueled by the urgent need to bridge the global healthcare workforce gap, with the World Health Organization projecting a shortfall of 11 million health workers by 2030.

Amid the increasing demand for healthcare app development, delivering precision and uncompromising quality has become more important than ever to safeguard patient safety, uphold regulatory compliance, and boost operational efficiency.

To get there, meticulous healthcare software testing plays a big role by validating functionality, securing sensitive data, optimizing performance, etc., ultimately cultivating a resilient and reliable healthcare ecosystem.

This piece delves into the core aspects of healthcare software testing, from key testing types and testing plan design to common challenges, best practices, and emerging trends.

Let’s get cracking!

What is Healthcare Software Testing?

Healthcare software testing verifies the quality, functionality, performance, and security of applications to align with industry standards. These applications can be anything from electronic health records (EHR), telemedicine platforms, and medical imaging systems to clinical decision-support tools.

What is Healthcare Software Testing

What is Healthcare Software Testing?

Given that healthcare software handles sensitive patient data and interacts with various systems, consistent performance and safety are of utmost importance for both patients and healthcare providers. Unresolved defects could disrupt care delivery and negatively affect patient health as well as operational efficiency.

Essentially, this process evaluates functionality, security, interoperability, performance, regulatory compliance, etc.

The following section will discuss these components in greater depth.

Learn more: 

5 Key Components of Healthcare Software Testing

5 Key Components of Healthcare Software Testing

5 Key Components of Healthcare Software Testing

Functional testing

Functional testing verifies whether the software’s primary features fulfill predefined requirements from the development phase. This initial step confirms that essential functions operate as intended before moving on to more complex scenarios.

Basically, it involves evaluating data accuracy and consistency, operational logic and sequence, as well as the integration and compatibility of features.

Security and compliance testing

Compliance testing plays a crucial role in protecting sensitive patient data and guaranteeing strict adherence to regulations in the healthcare industry.

Healthcare software, which often handles electronic protected health information (ePHI), must comply with strict security standards such as those outlined by HIPAA or GDPR. Through compliance testing, the software is meticulously assessed so that it meets these security requirements.

Besides, testers also perform security testing by assessing the software’s security features, including access controls, data encryption, and audit controls for full protection and regulatory compliance.

Performance testing

Performance testing measures the software’s stability and responsiveness under both normal and peak traffic conditions. This evaluation confirms the healthcare system maintains consistent functionality under varying workloads.

Key metrics include system speed, scalability, availability, and transaction response time.

Interoperability testing

Interoperability testing verifies that healthcare applications exchange data consistently with other systems, following standards such as HL7, FHIR, and DICOM. This process focuses on 2 primary areas:

  • Functional interoperability validates that data exchanges are accurate, complete, and correctly interpreted between systems.
  • Technical interoperability assesses compatibility between data formats and communication protocols, preventing data corruption and transmission failures.

Usability and user experience testing

Usability and user experience testing evaluate how efficiently users, including healthcare professionals and patients, interact with the software. This component reviews interface intuitiveness, workflow efficiency, and overall user satisfaction.

How to Design an Effective Healthcare Software Testing Plan?

A test plan is a detailed document that outlines the approach, scope, resources, schedule, and activities required to assess a software application or system. It serves as a strategic roadmap, guiding the testing team through the development lifecycle.

Although the specifics may differ across various healthcare software types – such as EHR, hospital information systems (HIS), telemedicine platforms, and software as a medical device (SaMD), designing testing plans for medical software generally goes through 4 key stages as follows:

How to Design an Effective Healthcare Software Testing Plan?

How to Design an Effective Healthcare Software Testing Plan?

Step 1. Software requirement analysis 

Analyzing the software requirement forms the foundation of a successful healthcare app testing plan.

Here, healthcare organizations should focus on:

  • Scrutinizing requirements: Analysts must thoroughly review documented requirements to identify ambiguities, inconsistencies, or gaps.
  • Reviewing testability: Every requirement must be measurable and testable. Vague or immeasurable criteria should be refined instantly.
  • Risk identification and mitigation: Identify potential risks, such as resource constraints and unclear requirements, then develop a mitigation plan to drive project success.

Step 2. Test planning 

With clear requirements, healthcare organizations may proceed to plan testing phases.

A well-structured healthcare testing plan includes:

  • Testing objectives: Define goals, e.g., regulatory compliance and functionality validation.
  • Testing types: Specify required tests, including functionality, usability, and security testing.
  • Testing schedule: Establish a realistic timeline for each phase to meet deadlines.
  • Resource allocation: Allocate personnel, roles, and responsibilities.
  • Test automation strategy: Evaluate automation feasibility to boost efficiency and consistency.
  • Testing metrics: Determine metrics to measure effectiveness, e.g., defect rates and test case coverage.

Step 3. Test design

During the test design phase, engineers translate the testing strategy into actionable steps to prepare for execution down the line.

Important tasks to be checked off the list include:

  • Preparing the test environment: Set up hardware and software to match compatibility and simulate the production environment. Generate realistic test data and replicate the healthcare facility’s network infrastructure.
  • Crafting test scenarios and cases: Develop detailed test cases outlining user actions, expected system behavior, and evaluation criteria.
  • Assembling the testing toolkit: Equip the team with necessary tools, such as defect-tracking software and communication platforms.
  • Harnessing automated software testing in healthcare (optional): Use automation testing tools and frameworks for repetitive or regression testing to improve efficiency.

Step 4. Test execution and results reporting

In the final phase, the engineering team executes the designed tests and records results from the healthcare software assessment.

This stage generally revolves around:

  • Executing and maintaining tests: The team conducts manual testing to find issues like incorrect calculations, missing functionalities, and confusing user interfaces. Alternatively, test automation can be employed for better efficiency.
  • Defect detection and reporting: Engineers search for and document software bugs, glitches, or errors that could negatively impact patient safety or disrupt medical care. Clear documentation should detail steps to reproduce the issue and its potential impact.
  • Validating fixes and regression prevention: Once defects are addressed, testing professionals re-run test cases to confirm resolution. Broader testing may also be needed to make sure new changes do not unintentionally introduce issues in other functionalities.
  • Communication and reporting: Results are communicated through detailed reports, highlighting the number of tests conducted, defects found, and overall progress. A few key performance indicators (KPIs) to report are defect detection rates, test case coverage, and resolution times for critical issues.

Learn more: How to create a test plan? Components, steps, and template 

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Key Challenges in Testing Healthcare Software and How to Overcome Them

Software testing in healthcare is a high-stakes endeavor, demanding precision and adherence to rigorous standards. Given the critical nature of the industry, even minor errors can have severe consequences.

Below, we discuss 5 significant challenges in healthcare domain testing and provide practical strategies to overcome them.

Key Challenges in Testing Healthcare Software and How to Overcome Them

Key Challenges in Testing Healthcare Software and How to Overcome Them

Security and privacy

Healthcare software manages sensitive patient data, making security a non-negotiable priority. Studies show that 30% of users would adopt digital health solutions more readily if they had greater confidence in data security and privacy.

Still, security testing in healthcare is inherently complex. QA teams must navigate intricate systems, comply with strict regulations like HIPAA and GDPR, and address potential vulnerabilities.

Various challenges emerge to hinder this process, including the software’s complexity, limited access to live patient data, and integration with other systems.

To mitigate these issues, organizations should employ robust encryption, conduct regular vulnerability assessments, and use anonymized data for testing while maintaining compliance with regulatory standards.

Hardware integration 

Healthcare software often interfaces with medical devices, sensors, and monitoring equipment, thus, hardware integration testing is of great importance.

Yet, a common hurdle is the QA team’s limited access to necessary hardware devices, along with the devices’ restricted interoperability, which make it difficult to conduct comprehensive testing. Guaranteeing compliance with privacy and security protocols adds another layer of complexity.

To address these challenges, organizations should collaborate with hardware providers to gain access to devices, simulate hardware environments when necessary, and prioritize compliance throughout the testing process.

Interoperability between systems

Seamless data exchange between healthcare systems, devices, and organizations is critical for delivering high-quality care. Poor interoperability can lead to serious medical errors, with research indicating that 80% of such errors result from miscommunication during patient care transitions.

Testing interoperability poses significant challenges because of the complexity of healthcare systems, the use of diverse technologies, and the need to handle large volumes of sensitive data securely. 

To overcome these obstacles, organizations are recommended to create detailed testing strategies, use standardized protocols like HL7 and FHIR, and follow strong data security practices.

Regulatory compliance

Healthcare software must comply with many different regulations, which also vary by region. Non-compliance can result in hefty fines and damage to an organization’s reputation.

Important regulations to abide by include HIPAA in the U.S., GDPR in the EU, FDA requirements for medical devices, and ISO 13485 for quality management systems.

What’s the Cost of Healthcare Application Testing?

The cost of software testing in healthcare domain is not a fixed figure but rather a variable influenced by multiple factors. Understanding these elements can help organizations plan and allocate resources effectively.

Here, we dive into 5 major drivers that shape the expenses of healthcare testing services and their impact on the overall budget.

What’s the Cost of Healthcare Application Testing

What’s the Cost of Healthcare Application Testing?

Application complexity

The more complex the healthcare application, the higher the testing costs.

Obviously, applications featuring advanced functionalities like EHR integration, real-time data monitoring, telemedicine capabilities, and prescription management require extensive testing efforts. These features demand rigorous validation of platform compatibility, data security protocols, regulatory compliance, seamless integration with existing systems, etc., all of which contribute to increased time and expenses.

Team size & specific roles

A healthcare application project needs a diverse team, including project managers, business analysts, UI/UX designers, QA engineers, and developers. 

Team size and expertise can greatly impact costs. While a mix of junior and senior professionals may be able to maintain quality, it complicates cost estimation. On the other hand, experienced specialists may charge higher rates, but their efficiency and precision often result in better outcomes and lower long-term expenses.

Regulatory compliance and interoperability

Healthcare applications must adhere to stringent regulations, and upholding them means implementing robust security measures, conducting regular audits, and sometimes seeking legal guidance – all of which add to testing costs.

What’s more, interoperability with other healthcare systems and devices introduces further complexity, as it requires thorough validation of data exchange and functionality across multiple platforms.

Testing tools implementation

The tools and environments used for testing healthcare applications also play a critical role in determining costs.

Different types of testing – such as functional, performance, and security testing – require specialized tools, which can be expensive to acquire and maintain.

If the testing team lacks access to these resources or a dedicated testing environment, they may need to rent or purchase them, driving up expenses further.

Outsourcing and insourcing balance

The decision to outsource software testing or maintain an in-house team has a significant impact on costs.

In-house teams demand ongoing expenses like salaries, benefits, and workspace, while outsourcing proves to be a more flexible and cost-effective solution. Rates of outsourcing healthcare software testing services vary depending on the vendor and location, but it often provides access to specialized expertise and scalable resources, making it an attractive option for many healthcare organizations.

Learn more: How much does software testing cost and how to optimize it?

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Best Practices for Healthcare Software Testing

Delivering secure, compliant, and user-centric healthcare software necessitates a rigorous and methodical approach.

Below are 5 proven strategies to better carry out healthcare QA while addressing the unique complexities of this sector.

Best Practices for Healthcare Software Testing

Best Practices for Healthcare Software Testing

Conduct comprehensive healthcare system analysis

To establish a robust foundation for testing, teams must first conduct a thorough analysis of the healthcare ecosystem in which the software will operate. This involves evaluating existing applications, integration requirements, and user expectations from clinicians, patients, and administrative staff. 

On top of that, continuous monitoring of regulatory frameworks, such as HIPAA, GDPR, and FDA guidelines, is required to stay compliant as industry standards evolve. By understanding these dynamics, healthcare organizations can design testing protocols that reflect real-world clinical workflows and anticipate potential risks.

Work with healthcare providers

Building on this foundational analysis is only the first step; partnering with healthcare professionals such as clinicians, nurses, and administrators yields invaluable practical insights.

These experts offer firsthand perspectives on usability challenges and clinical risks that purely technical evaluations might overlook. For instance, involving physicians in usability testing can uncover inefficiencies in patient data entry workflows or gaps in medication alert systems.

As a result, fostering close collaboration between healthcare providers and testers and actively engaging them throughout the testing process elevates the final product quality, where user needs are met and seamless adoption is achieved.

Employ synthetic data for risk-free validation

Software testing in healthcare domain on a completed or nearly finished product often requires large datasets to evaluate various scenarios and use cases. While many teams use real patient data to make testing more realistic, this practice can risk the security and privacy of sensitive information if the product contains undetected vulnerabilities.

Using mock data in the appropriate format provides comparable insights into the software’s performance without putting patient information at risk.

Furthermore, synthetic data empowers teams to simulate edge cases, stress-test system resilience, and evaluate interoperability in ways that may not be possible with real patient data alone.

Define actionable quality metrics

To measure the performance of testing efforts, organizations must track metrics that directly correlate with clinical safety and operational efficiency. Some of these key indicators are critical defect resolution time, regulatory compliance gaps, and user acceptance rates during trials. 

These metrics not only highlight systemic weaknesses but also suggest improvements that impact patient outcomes. For instance, a high rate of unresolved critical defects signals the need for better risk assessment protocols, while low user acceptance rates may indicate usability flaws.

Software Testing Trends in Healthcare Domain

The healthcare technology landscape changes rapidly, demanding innovative approaches to software testing.

Here are 5 notable trends shaping the testing of healthcare applications:

Software Testing Trends in Healthcare Domain

Software Testing Trends in Healthcare Domain

Security testing as a non-negotiable

Modern healthcare software enables remote patient monitoring, real-time data access, and telemedicine – exposing large volumes of sensitive patient data, such as medical histories and treatment plans, to interconnected yet often fragile systems. Ensuring airtight data protection should thus be a top priority to safeguard patient privacy and prevent breaches.

Security testing now goes beyond basic vulnerability checks, emphasizing advanced threat detection, encryption validation, and compliance with regulations like HIPAA and GDPR. Organizations must thus thoroughly assess authentication protocols, data transmission safeguards, and access controls to find and address vulnerabilities that could jeopardize patient information.

Managing big data with precision

Modern healthcare applications process and transmit vast amounts of patient data across multiple systems and platforms. These applications are built with dedicated features to facilitate data collection, storage, access, and transfer. Consequently, testing next-generation healthcare applications requires considering the entire patient data management process across various technologies. In doing so, they must guarantee that data flows smoothly between systems while maintaining efficiency and security.

Still, comprehensive testing remains essential to verify proper data management, necessary to verify that patient data is managed properly, including mandatory tests for security, performance, and compliance standards.

Adopting agile and DevOps practices

To meet demands for faster innovation, healthcare organizations are increasingly embracing agile and DevOps methodologies.

Agile testing integrates QA into every development sprint, allowing for continuous feedback and iterative improvements. Meanwhile, DevOps further simplifies this process by automating regression tests, deployments, and compliance checks.

Expanding mobile and cross-platform compatibility testing

With a growing number of users, including patients and healthcare professionals, accessing healthcare solutions through smartphones and tablets, organizations are increasingly prioritizing mobile accessibility.

Testing strategies must adapt to this shift by thoroughly evaluating the application’s functionality, performance, and security across various devices, networks, and operating environments.

Leveraging domain-skilled testing experts

Healthcare software complexity requires testers with specialized domain knowledge, including a deep understanding of clinical workflows, regulatory standards like HL7 and FHIR, and healthcare-specific risk scenarios.

For instance, testers with HIPAA expertise can identify gaps in audit trails, while those proficient in clinical decision support systems (CDSS) can validate the accuracy of alerts and recommendations.

To secure these experts on board, organizations are either investing in upskilling their in-house QA teams or partnering with offshore software testing vendors who bring extensive knowledge in healthcare interoperability, compliance, patient safety protocols, and so much more.

Read more: Top 5 mobile testing trends in 2025

FAQs about Software Testing in Healthcare

What types of testing are often used for healthcare QA?

A comprehensive healthcare QA strategy typically involves multiple testing types. The most commonly used testing types are functional testing, performance testing, usability testing, compatibility testing, accessibility testing, integration testing, and security testing.

Which are some healthcare software examples used in hospitals?

Hospitals use various software, including electronic health records, telemedicine apps, personal health records, remote patient monitoring, mHealth apps, medical billing software, and health tracking tools, among other things.

What’s the cost of healthcare application testing?

The cost of testing healthcare software depends on application complexity, team size, regulatory compliance, testing tools implementation, and outsourcing vs insourcing. Generally, mid-range projects range from $30,000 to $100,000+.

What are some software testing trends in the healthcare domain?

Current healthcare software testing trends include security-first testing to counter cyber threats, Agile/DevOps integration for faster releases, big data management, domain-skilled talent, and mobile compatibility checks.

Partnering with LQA – Your Trusted Healthcare Software Testing Expert 

The intricate nature of healthcare systems and sensitive patient data demands meticulous software testing to deliver reliable solutions.

A comprehensive testing strategy often encompasses functional testing to validate business logic, security testing to protect data, performance testing to evaluate system efficiency, and compatibility testing across various platforms. Accessibility and integration testing further boost user inclusivity and seamless interoperability.

That being said, several challenges emerge during the testing process. To encounter such hurdles, it’s important to comprehensively analyze healthcare systems, partner with healthcare providers, use synthetic data, determine actionable quality metrics, and stay updated with the latest testing trends.

At LQA, our team of experienced QA professionals combines deep healthcare domain knowledge with proven testing expertise to help healthcare businesses deliver secure, high-quality software that meets regulatory requirements and exceeds industry standards.

Contact us now to experience our top-notch healthcare software testing services firsthand.