Learn, Generate, & Improve Code With Genislab AI

Functional testing

Turn to functional testing in order to:

Ensure your product is developed in line with the requirements

Release the application that resonates with its end users

Get a professional assessment of software with a complex business logic


Functional testing services that cover it all

Our company delivers the full scope of functional testing services, including:

Smoke testing

The very first step of the testing process, this one is to make sure the most important features work well, before proceeding with further testing.

Interface testing

We test the user interface functionality to check how fully and correctly it meets the requirements.

Requirements-based testing

We design a precise set of test cases based on the software requirements that we analyze and validate.

Integration testing

We detect defects occurring in the interfaces as well as in the functioning of integrated components or systems as a whole.

Exploratory testing

We extend the test coverage by going beyond the predefined test suite, using cognitive thinking and clearly understanding particular software specifics and end users’ behavior.

System testing

We carry out functional system testing for all of the integrated components to evaluate how the system’s behavior corresponds to the specification.

Regression testing

We assure the changes made to the system haven’t affected its flawlessly working functionality.

Acceptance testing

We ascertain that consumers can use the software to effectively perform the tasks for which the software was built.


Contact our team
Get started with your functional software testing.


Functional testing for all types of software

Whether you need web functionality testing or quality assurance of any other specific system, be sure Genislab’s team has proven methods for testing a range of solutions:

Web apps

Mobile apps

Desktop apps

System utilities

Embedded systems

IoT devices and apps

Big data & analytics


As a result, you get:

Independent software quality assessment

Transparent work processes and smooth communication

Comprehensible testing artifacts and results along with detailed reporting

Detected critical issues before the go-live

The deadlines and QA budgets met

Why Genislab?

Unique approach to training

Group based learning and training for mobile and web services testing, and more to provide our clients with effective and reliable QA solutions.

Broad domain expertise

Our professional team of software testing engineers has successfully completed 100+ projects across multiple industries.

Effective management

We maintain a full control over the testing process along the entire SDLC.


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,