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Role of Artificial Intelligence in Performance testing and Engineering

A typical Performance Testing starts with analyzing the application UI and creating the test scripts. Post that users hit the application server and generate beautiful dashboards from Load testing tools indicating the Response time, Throughput, CPU utilization time, memory utilization etc.

In the era of AI (Artificial Intelligence) powered softwares, during the early stages of application design, performance engineers should be able to answer questions like:  What should we expect once the application is in production? Where are the potential bottlenecks? How to tune application parameters to maximize performance?

Critical applications need a mature approach to Performance testing and monitoring. AI is the intelligent part of Performance Testing process. It acts as brain in the process. Daily Tasks like test design, Scripting and implementation can be handled using AI, so that test engineers can focus on creative side of software testing.

One reasonable use case of using AI in PT (Performance Testing) can be codeless automation script. Writing performance scripts using Natural Language Processing(NLP) can make the scripting task way easier. In this type of testing, computers learn from the data given to them without programming it. Below are the aspects of solution empowered by AI-ML (Artificial Intelligence- Machine Learning) in performance testing:

  • The testing environment developed using ML, will have advanced capabilities in terms of self-healing and intuitive dashboarding. using deep learning algorithms, the corrections can be handled automatically.
  • The test flows are recorded and can be tested using data. No coding required in most of the scenarios.
  • Reusable functions and objects can be generated and grouped using semi-supervised learning. Scenarios are flow-based, and thus the implementation is transparent to user.

Yet another use case would be performance test modelling processes. AI's pattern recognition strength can extract relevant patterns while load testing which is very useful for modelling performance process. The PT model consists of the algorithms being used, from which AI learns from the given data. The ability of AI to anticipate future load problems helps in creating Performance test model efficiently. It deals with lot of data and can predict the system failures. Once the system data is analyzed, Performance test model can be created based on the system behavior.

Another area can be SLA design. SLAs should be measurable, attainable, simple, realistic and time bound, but most SLA are not designed like this. This is the basic limitation of human powered systems. However, once AI takes the role, the situation will change. It can track all the affecting areas and gets reinforced into monitoring system with providing granularity. It can analyze the complexity of the system and suggest the appropriate SLA. For example, if the lines of code are 1000 then SLA can be considered as 500 milliseconds. AI can detect working trends in a system directly, as system performance changes, SLA can fine-tune in real time.

 Monitoring tools like Dynatrace, AppDynamics introduced AI into their system which are helping in identifying the bottlenecks in multiple tiers of applications in early stages of software development. It can analyze the application and can predict the performance defects at the code level. Many open source tools like webpage test, GTmetrix, Yslow pinpoint specific problems like server request issues and help engineers to solve the issues quickly. Automation Tools like Test.ai is useful in getting the performance metrics of your application as well.

Role of AI in every phase of performance testing and engineering is proved very beneficial and is future of performance testing. Use of AI in performance testing will make tasks like scripting, monitoring highly impactful and help to get real time results very quickly. I believe, in future role of AI in performance testing will be a game changer!

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