Learn, Generate, & Improve Code With Genislab AI

Microservices testing
 

Microservices testing

Make your ecosystem faultless and secure at a granular level

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Why opt for testing microservices?

Performance

Enable smooth interaction and unhindered data flow between microservices, with no bottlenecks and errors

Security

Make certain there are no vulnerabilities to serve as a foothold for cybersecurity breaches

Sustainability

Get expert recommendations on how to make your microservice architecture resilient and future-proof

Efficiency

Guarantee each microservice delivers intended functionality to end users

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One vendor, a variety of testing strategies

By leveraging a range of testing approaches, be it for manual or automated testing of microservices, the Genislab team will help you get microservices that maximize deployment velocity.

Unit testing

Our team will test the source code units and check if they behave as designed with different inputs and under varied conditions.

Component testing

As part of our testing routine, we will isolate each component of your microservice architecture using stubbing and mocking techniques to identify possible flaws and bugs in its code prior to and after deployment.

Integration testing

When running microservices through integration testing, we will review how well they connect, communicate, and interact as a unified system. Our team will also perform microservices testing through API to ensure integrated modules meet performance and reliability expectations.

Performance testing

We render thorough microservices performance testing, assessing how stable and responsive the architecture is under expected and extreme loads, and recommending how to improve its fault-tolerance. We also identify bottlenecks in terms of load balancing in order to leverage computing power usage.

Contract testing

The Genislab team will simulate interaction scenarios between consumer and API provider endpoints of microservices to detect possible malfunctions and discrepancies between expected and returned responses.

Security testing

Within the scope of microservices security testing, we will do an assortment of checks – from vulnerability scanning and penetration testing to risk assessment, reviewing user authentication, security patches, and API gateway safety, among other aspects.

End-to-end testing

Applying one overarching testing strategy, we will make sure your entire software product, which is based on microservices architecture, delivers as expected while meeting rigorous quality requirements, from low-level services to public APIs and the front-end.

Test automation

For agile-led projects where continuous deployment is critical, we implement a continuous testing approach to keep your system in check during fast-paced delivery.

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Contact our team
Contact us to get a free consultation on your microservices QA.

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Your environment is safer with us

Bespoke service

Genislab will support you at any stage, tailoring testing solutions to address specific goals, challenges, and constraints. 

Top testing tools

Our team has access to best-of-breed tools and frameworks for testing microservices – Hoverfly, Pact, Swagger, EnvoyWireMock, and more.

Always on the same page

We will work closely with your team to determine the scope and provide regular updates on the progress made and results delivered.

Focus on quality and security

Our team carries out testing in full compliance with quality standards such as ISO 9001 and ISO 27001, as well as specific regulations as required.

 

How do we utilize AI to increase the quality of the solution? We distinguish between three
potential applications, namely translation, search and sequence anticipation and analysis
and elaborate on each of them below. Some of the examples we provide implement more
than one of the possible applications of AI to achieve their goal.

Translation means that one artifact is converted by the AI to another artifact.
 
API specifications, namely swagger documentation of the APIs, are translated into executing
code that tests the APIs. This can serve to ease the task of creating ATDD acceptance tests
once the interfaces of the APIs are defined.
 
 
Another translation could cluster an existing
set of regression tests in order to minimize them
 
yet another translates requirements to
their associated risk.
 
We envision that many more translations are possible, especially with the introduction of large language models new use cases will be explored. For example,

it is possible with state of the art large language models to translate description of a low
level programming task to useful code snippets

Using AI to search is a classical application of AI to testing.

For example genetic algorithms are used to test the solution for security vulnerabilities, e.g., AI search has also been applied to non-functional chaos directed test generation  and to functional
testing of APIs .

Many time the search is guided by an implicit or explicit coverage model that serves to guide the search. For example, in the case of security vulnerabilities models of code coverage are applied.

Generate test cases automatically based on the natural language input, ensuring comprehensive test coverage

  • Product guide generation
  • Deployment Auditing and Evaluation of Models
  • Audio Transcription
  • Labeling Data Translation Models
  • Pre-Training
  • Semantic Search
  • Increasing Inference Speed
  • Image Reducing Latency of Models

The last application of AI occurs through sequences and analysis.

Anomaly detection has been applied solution logs in order to analyze and debug the system. Even more exciting large language models with generative capabilities are attempting to anticipate the next program statement a programmer enters with some success.

Anomaly detection has nuances and a range of approaches that apply. For example, a black Friday activity on the system represents a change in the way the system is used but is not a stable ongoing change but a single abrupt occurrence. Techniques have been developed to identify change that is stable over time and requires a change in the solution. They are name drift analysis,