Quality Engineering & Testing for Digital Health Companies

We do Quality Engineering for Wearables, Biosensors, and Companion Applications

Contact Us – 4253012760

How We Help

Digital Health Pioneers

Set benchmarks for customer experience through mobile applications and hardware device testing & quality engineering services.

digital health portal and companion app testing

Digital Health Portal & Companion App Testing

Developing a user-friendly regulatory compliant websites, portals, and health app requires considerable consideration. Our team develops & executes test plans for our client apps

Firmware & Embedded Testing

Firmware & Embedded Testing

Our engineering team develops destructive and non-destructive custom test plans and test beds to put your devices to the test, including battery utilization monitoring, sensor testing, and communication & connectivity testing

Compliance Testing

Compliance Testing

We make sure your project meets or exceeds regulatory compliance standards, including FDA, HIPAA, HITECH, ADA, CCPA, GDPR, PIPEDA and AODA

device lab

Device Lab

We maintain a state-of-the-art mobile device lab allowing our clients to test their wearables & mHealth companion apps across current smartphone devices from major manufacturers

Did You Know?

SAMSUNG,ย GOOGLE,ย APPLEย AND OTHER MOBILE PHONE MANUFACTURERS MANAGE BLUETOOTH CONNECTIVITY AND COMPANION APPS DIFFERENTLY. IT IS IMPORTANT TO TEST YOUR DEVICE ACROSS VARIOUS GENERATIONS OF SMARTPHONES TO ENSURE COMPATIBILITY.

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Organizations thatย adoptย artificial intelligence (AI) in testing of microservices-based applications gain better accuracy, faster results, and greater operational efficiency. AI and machine-learning technologiesย have matured over the lastย few years, and todayย their application in automated testingย can help in more ways than one. In fact, AI has redefined the way microservices-based applications are tested, especially when it comes toย canary testing. The introduction of AI in software testingย helps both developers and testers alike. It improves accuracy;ย the same steps can be performed accurately every time they’re needed. Automated testing can increase both the depth and scope of your tests, resulting in more thoroughย overall test coverage. You can also leverage AI to simulate a large number of users interacting with your application. Here’s how AI-enabledย automation can help you test as youย scale microservices-based applications, as well as the challenges you’ll faceย and effective strategies you can adopt to overcome them.

Why traditional testing strategies don’t work

Traditionally, whenย creatingย monolithic applications, you’dย test each unit ofย codeย with unit tests. As different components of the application are joined together, you typicallyย test your application using integration testing first and, usually, system testing, regression testing, and user acceptance testing follow. If the code passes all of these tests, the releaseย goes out. Testing microservices-based applicationsย is not an easy task and is not the same as testing monoliths; you must be aware of not only the service you are testing but also its dependenciesโ€”the services that work with the services under test. Owing to the granular nature of microservices architecture, boundaries that were previously hidden in a traditionalย applicationย are exposed. You might have several different teams spread across geographical distances working simultaneously on different services;ย this makes coordination extremely challenging. It can beย difficult to find a particular time window to perform end-to-end testing of the application as a whole. The distributed nature of microservices-based development poses many challenges toย testing your application. Theseย include:
  • Availability:ย Because of the distributed nature of microservices architecture, it is difficult to find a time when all microservices are available.
  • Isolation:ย Microservices are designed to work in isolation together with other loosely coupled services. This implies that you should be able to test every component in isolation as well as testing them together.
  • Knowledge gap:ย You should possess a strong knowledge of each microservice;ย this would help you to write effective test cases.
  • Data:ย Each microservice can have its own copy of data. In other words,ย eachย can have its own copy of the database, which may be different from another microservice’s copy. As a result, data integrity poses a challenge.
  • Transactionality:ย Unlike with a monolith, where transactionality is often assured at the database level, implementing transactionality between different microservices is challenging, because a transaction can consist of various service calls spread across different servers.
Typically, a microservices-based application consists of several services, each of which can dynamically scale up if needed. There is also a risk of failure and the cost of fixing bugs orย issues after integration. Hence, you should have an effective test strategy in place for testing microservices-based applications.

How to build an effective testing strategy

To build an automatedย testing process for a microservices-based application, you shouldย follow the same best practices you would for any other type of testing:
  • ย Understand the customer’s expectations as far as test automation is concerned.
  • Set quality goalsโ€”and adhere to them.
  • Analyze the testing types that are right for you to achieve the goals.
  • Write tests according to the test pyramid (i.e., considering that the cost of the tests increases as you move up the pyramid).

AI-driven test automation: Embrace innovation

Today’s software testers canย take advantage of AIย for test creation, test execution, and data analysis by using natural-language processing and advanced modeling techniques.ย AI-based sofwareย testing can helpย by increasing efficiency, facilitating faster releases, improving test accuracy and coverage, and allowing for easier test maintenance, particularly when it comes to managing your test data. For efficient test maintenance, you need toย knowย what is happening to your dataย at the time of test creation. Inadequate data modeling is one reasonย why test maintenance fails,ย becomingย a bottleneck in your deployment pipeline. AI can help with efficient data modeling and with root-cause analysis. Repeating tests manually each time the source code changesย can be time-consuming and costly. Once you create automated tests, you execute themย repeatedly and quickly with no additional cost.

Use AI for canary testing

Canary testingย helps reduce risks by gradually rolling out the changes to a small group of users before presenting itย to a larger audienceโ€”and it is particularly useful in the testing of microservices-based applications. In a typicalย application, the changes toย microservices happen independently of one another, so thoseย microservices need toย be verified independently as well. AI can help automateย canary testing ofย microservices-based applications. You can take advantage of AI concepts such as deepย learning to identify the changes in the new code and the issues withinย it.ย AI can be used to compare the experience of the small group of usersย with that of the existing users, and this can be done automatically;ย you don’t need any human intervention in the loop.

Challenges in AI-based microservices testing

AI-based testing does have some constraints. While you can automate functional and unit tests, it is quite difficult to automate integration tests, because of complexity. Some of the other challenges in AI-based testing include the following:

Skills

Testing microservices-based applications with an AI-based approach requires extensive technical expertise from testers, and is very different from whatย manual or automation testersย are used to. Testers should be adept at how to useย AI-based tools specifically forย microservices-based applications.

Use cases

Learn howย to determine the best use cases for using AI in microservicesย test automation. Oneย is to use AI forย creating yourย unit tests. You can take advantage of AI to perform static code analysis and determine the portions of code that are not covered by your unit tests. You can also use AI to update unit tests as soon as the source code changes, as well as forย test creation, execution,ย data analysis, and API testing in microservices-based applications. AI can help you understand the patterns and relationships inย API calls andย come up with more advanced patterns and inputs for testing the API. You can leverage an AI-powered continuous testing process to more efficiently detect the controls that have changed.

AI-based testing can’t do everything

AI-based test automation of microservices canย create more reliable tests, and in so doingย slash the time needed for test creation, maintenance, and analysis. Such tests can in turn be used to check the service-to-service communication, test communication paths, etc. You can alsoย leverage deep-learningย models and other AI techniques to empower your team to build tests faster andย executeย them at scale in the cloud. Adopting AI forย microservicesย test automation is noย panacea. It won’t magically eliminate all problems associated with software testing. But it can help you make your software testing process smarter, more efficient, and fasterโ€”and thereby deliver business value consistently.
 

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Quality Engineering Services

For Digital Health Companies

QE consulting

QE Consulting

We work with your existing Quality Assurance or Quality Engineering Partner to develop test strategies, identify requirements, and expected outcomes of quality engineering projects

Functional Testing

Functional Testing

Make sure your project meets your deliverable goals by validating that all user requirements, functions and features meet specifications

performance testing

Performance Testing

Speed kills user experience and revenue opportunities fast. Users expect your app, device or platform to run quickly. We put your project under a stress test to make sure it can handle real-world scenarios and fringe use cases

Test Automation

Test Automation

We help our clients get to market faster using automated AI/ML testing platforms designed to find critical application errors quickly, pushing requirements back to developers for resolution

regression testing

Regression Testing

New changes, updates and features can break your app or device. Regression tests existing functionality, features and user experience are maintained from version to version.

Security Testing

Developing regulatory-compliant solutions for digital health requires a lot of security considerations. We help make sure that your app, platform and wearable devices maintain the highest level of security while in use, in transit and in rest.