Learn, Generate, & Improve Code With Genislab AI

Cloud testing
 

Cloud testing

Safeguard your cloud as well as on-premises software against faults, bottlenecks, and security threats
Our work embraces everything cloud: we put to test the functionality and integrity of cloud applications, as well as apply cloud-based testing tools for quality assurance of both cloud-based and installed applications.

Testing all cloud software models

Whatever cloud model you choose to go with, the Genislab team knows the soft spots of each – as well as how to detect them.

SaaS

We’ll subject your SaaS to a meticulous review and search out its interoperability flaws, customization-inflicted vulnerabilities, and potential downtime or breach factors.

PaaS

Testing your PaaS, our team will pay close attention to the issues likely to arise from integration with legacy systems, vendor lock-in, and provider-controlled data storage.

IaaS

Within the IaaS testing process, the Genislab team will look into your software’s multi-tenant security, runtime botches, and personalization faults.

A full set of cloud testing services

With a variety of testing strategies, our team will make sure to inspect every critical aspect of your cloud application.

Functional testing

We will review your cloud software feature by feature and verify whether it complies with the set requirements, integrates seamlessly with your corporate environment, and meets users’ expectations.

Performance testing

In the course of cloud performance testing, the Genislab team will check your virtual environment for its resilience to stress and load, endurance, and network latency to detect weak points in its capacity and scalability. 

Security testing

Our team will validate security controls of your application, evaluate its susceptibility to a range of attacks via cloud penetration testing, and deliver a detailed vulnerabilities report upon completion.

Compatibility testing

We will investigate whether your cloud application provides an equally good experience across devices and put to test its compatibility with various hardware, operating systems, and browsers.

Usability testing

Through user behavior simulation tests, the Genislab team will discover possible flaws, inconsistencies and botches in your cloudware workflows and interfaces.

Test automation

Leveraging Selenium and other suitable frameworks, we will set up a cloud automation testing environment to continuously scan for internal and external vulnerabilities and evaluate compliance with set standards.

Contact our team
Get in touch to discuss your cloud software quality.

We unlock the value of cloud-based testing

Rigorous software quality assurance

  • Stable operation. Ensure consistent performance under ordinary and extreme conditions.
  • Risk-free customization. Make sure no configuration or scale-up causes functional hiccups.
  • Data safety. Hedge against disruptive malware, data theft, and compliance violations.
  • User focus. Determine how intuitive and seamless your product’s UX/UI are.
  • Expert recommendations. Gain viable advice on how to render your cloud architecture fault-tolerant and tamper-proof.

Higher testing efficiency

  • Scalability. Upscale or downscale computing resources to test against a complete set of scenarios.
  • Increased testing coverage. Emulate any extreme loads and conditions as well as simulate multiple user interaction paths.
  • Round-the-clock access. Opt for ongoing quality assurance with 24/7 available testing resources.
  • Cost-efficiency. Save funds on dedicated hardware and software with the pre-configured virtual testing environments.
 

Your cloud testing team at a glance

Always up to the challenge

The Genislab team is ready to render cloud computing testing services at any stage of the development process.

Tailored solutions

Our team will delve into the specifics of your software and devise a bespoke solution-driven testing strategy.

Blanket compliance

We deliver cloud testing strictly within global data security protection and privacy laws (PCI DSS, HIPAA, FISMA, etc.), and will take note of any case-specific regulations.

20 years of expertise

Having accumulated substantial testing insights across industries, we put to use only time-proven tools and strategies.

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,