Benefits of Using AI-ML in Performance testing – B – 069

Abstract

The Performance Testing starts with analysing the application UI and creating the test scripts. Post that users hit the application server and generate beautiful Results from Load testing tools indicating the Response time, Throughput, CPU utilization time, memory utilization etc. In the era of Artificial Intelligence (AI) and Machine Learning (ML) 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.

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 analysed, Performance test model can be created based on the system behaviour.

SLA design: SLAs should be SMART (Simple, Measurable, Attainable, 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 be change. It can track all the affecting areas and gets reinforced into monitoring system with providing granularity. It can analyse the complexity of the system and suggest the appropriate SLA 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 analyse the application and can predict the performance defects at the code level.

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!

Speaker Bio

BIO – Nithin joined IBM in the year March 2019 as a Senior Technical Service Specialist. With an overall experience of ~6 years, Worked as performance Test Analyst. Specialized in E2E performance Testing i.e. Planning to recommendation on tuning and optimizing performance for an Application Under Test. Tools Used: Load Runner, Apache JMeter, APICA APM Tools: AppDynamics.
Apart from work, My Hobbies are Pencil Drawing, Playing Sports like cricket, Badminton etc.