Adolf Patel

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Adolf Patel

Adolf has over 16 years of experience in Information Technology Industry in general which includes experience in Performance Testing & Engineering, Automation testing and QA consulting, currently working as a Performance test architect at one of the leading service based company.

I have an excellent knowledge and experience on process definition, performance engineering, Capacity Planning, Managing large scale projects, Framework development , tools standardization and business development. Well experienced in Agile methodology, CICD/DevOps framework implementation, Cloud based QA consulting, Mobile performance engineering and AI based QA solution architecting. Efficiency in prioritizing work and adopting suitable technology and best practices in projects to ensure quality of deliverables meet or surpass client expectations. I have worked at client place for more than a decade in various capacity which includes Client relationship manager, program delivery manager, QA process consultant, bringing new ideas and tools to standardize the quality engineering framework & methodology.

Adolf has worked on performance testing & Engineering assignments on many different technologies including Web applications, Thick Client application, Java/.NET applications, Web services, Batch applications, Mainframes, MQ’s, Cloud based applications etc. He is well versed in testing tools like HP Load runner, Apache JMeter, Neoload, NewRelic, Dynatrace, Splunk, vRops, Grafana, Kibana, HP Quick Test pro etc, and has excellent knowledge of test result analysis, recommendations, Profiling, diagnosing and capacity planning.

Title: Use of AI and ML in Performance Testing

Abstract: Technologies are changing like never and so are the software products. Agility and speed are not only limited to software development but also to software testing and when it comes to quality, performance testing and engineering are one of the ‘must-to-do activities before production rollout. More applications and products are becoming AI & ML enabled and hence it is the need of the hour to make our performance strategy align to AI and ML. We must start using AI and ML in the performance testing lifecycle. We like performance test results, tuning recommendations and capacity assessment at run time using a click on the software. Thanks to AI which makes it possible. AI is an intelligent part of the Performance Testing lifecycle. The role of AI in every phase of performance testing and engineering is proved very beneficial and is the future of performance testing. The question is when to apply AI and what we can achieve using AI? AI can be enabled as early as during requirement analysis. Below are some of the things we can achieve using AI-powered ML.

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