Learn, Generate, & Improve Code With Genislab AI

Performance testing
 

Performance testing

We integrate our performance testing services at any stage of the SDLC to ensure your system’s flawless operation

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What performance testing helps you discover

Performance testing allows preventing system flaws and unpredicted spending by helping you get the answers to the following questions:

Can your system withstand overload?

When scaled, how much will the system resources cost in the cloud?

Will the software remain operable in the long run?

What are the performance bottlenecks?

What is the maximum load possible?

Can the system sustain its everyday load?

 

Contact our team
We are the ones to ensure spotless performance for your software.

 

Genislab is there to help

Our QA team gets involved in all of the SDLC stages to help your software engineers focus on writing perfect code and creating a product with flawless performance. Throughout full-cycle performance testing, our company conducts various testing types to cover the specific needs of each client. These services include:

Load testing

We determine whether the system is able to cope with the required target load for an extended period of time.

Stress testing

We evaluate software performance under extreme load to identify the upper limits of its capacity.

Scalability testing

We define software effectiveness throughout processing power increase and architectural changes.

Volume testing

We measure software productivity under accumulating volumes of stored and processed data.

Stability testing

We perform longstanding testing for about 72 hours to evaluate system’s operability under a middle-level load.

Configuration testing

We verify performance under various software and hardware configurations.

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Software performance testing stands for business benefits

Our performance QA and testing bring value to your business with:

Managed software performance

  • Efficient performance monitoring
  • Application productivity control
  • Boosted application behavior

Reduced software TCO

  • Informed hardware investments
  • Reduced operational and maintenance costs
  • Shorter time to restore

 

Client-side performance testing: Genislab specialty

Our R&D lab has come up with our proprietary solution to the issues typically affecting customer experience, such as heavy interfaces, long response times, and poor performance overall. We call it client-side performance testing.

Optimized page loading

Accurate caching

Reduced server response times

No heavy-weight elements

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How client-side performance testing helps

In addition to server-side testing, we conduct client-side performance testing which allows to measure the load and render time for an HTML page, and detect defects that slow down the page load. Our QA team has extensive experience in automating client-side checks within any testing environment.

With client-side performance testing, customer experience can be improved through:

  • Measuring the loading speed of specific pages from different geographical regions, if required
  • Controlling the app speed after system updates take place
  • Identifying defects that slow down page loading and rendering
  • Detecting heavy-weight elements to optimize the amount of stored and transmitted data
 

Choose your testing scenario

To provide you with valuable results suitable for your particular needs, we offer several service packages:

Performance diagnostics

A one-off testing round showing if your system meets the requirements.

Actionable recommendations

We identify and recommend solutions to your performance issues, working on our own or in cooperation with your team.

End-to-end testing

We detect as well as resolve bottlenecks to ensure perfectly stable and error-free software performance.

 

Our expertise includes:

Systems and platforms

Web portals

Mobile applications

Big data

Distributed systems

Microservices

IoT

Cloud environments

Amazon Web Services

Microsoft Azure

Google Cloud

SaaS

SAP

Salesforce

 

We deliver performance testing services through:

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Why Genislab?

Being a devoted team of 1,100+ QA engineers with 20 years of testing experience, we are a reliable tech partner because of our:

Continuous learning

We keep gaining and sharing new knowledge at our proprietary QA Academy and stay innovative through our in-house R&D lab.

Vast experience

We bring you the skills and knowledge generated in over 200 projects both for basic systems and the ones with complex business logic.

Efficient teamwork

We adhere to the Agile approach with iterative deliverables and high-paced team collaboration.

Flexible approach

We employ a variety of tools and methodologies to create befitting stacks for specific needs.

 

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,