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#ATAGTR2020 Presentation - Adaptive Learner

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Arun Kumar Dhakshinamoorthy delivered a session on "Adaptive Learner" at ATAGTR2020

ATAGTR2020 was the 5th Edition of Global Testing Retreat.

Arun is a Performance Test Analyst from Cognizant Technology Solutions who has nearly 10 years of experience in Development and Quality Assurance.

The video recording of the session is now available on the following link: https://youtu.be/h5ou0B8a0oY

To know more about #ATAGTR2020, please visit: https://gtr.agiletestingalliance.org/

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#ATAGTR2020 Presentation - Adaptive Learner

  1. 1. #ATAGTR2020 As a author of this presentation I/we own the copyright and confirm the originality of the content. I/we allow Agile testing alliance to use the content for social media marketing, publishing it on ATA Blog or ATA social medial channels(Provided due credit is given to me/us) #ATAGTR2020 As a author of this presentation I/we own the copyright and confirm the originality of the content. I/we allow Agile testing alliance to use the content for social media marketing, publishing it on ATA Blog or ATA social medial channels(Provided due credit is given to me/us) Adaptive Learner Arun Kumar Dhakshinamoorthy
  2. 2. #ATAGTR2020 As a author of this presentation I/we own the copyright and confirm the originality of the content. I/we allow Agile testing alliance to use the content for social media marketing, publishing it on ATA Blog or ATA social medial channels(Provided due credit is given to me/us) #ATAGTR2020 As a author of this presentation I/we own the copyright and confirm the originality of the content. I/we allow Agile testing alliance to use the content for social media marketing, publishing it on ATA Blog or ATA social medial channels(Provided due credit is given to me/us) AGENDA What is Adaptive Learner Adaptive Learner in Performance Testing Lifecycle How it works ? Key Benefits & Conclusion Anomaly Detection Backpropagation using python Determination of Machine Learning Model
  3. 3. #ATAGTR2020 As a author of this presentation I/we own the copyright and confirm the originality of the content. I/we allow Agile testing alliance to use the content for social media marketing, publishing it on ATA Blog or ATA social medial channels(Provided due credit is given to me/us) #ATAGTR2020 As a author of this presentation I/we own the copyright and confirm the originality of the content. I/we allow Agile testing alliance to use the content for social media marketing, publishing it on ATA Blog or ATA social medial channels(Provided due credit is given to me/us) What is Adaptive Learner Adaptive Learner is a automated Solution which provides constant feedback in performance testing Automatically redefining the testing strategy Goal is to find a Performance Problem where application behave different for various combination of data Identify Bottlenecks or Hot spots where Performance is limited due to Software or Hardware configurations Sample Implementation Machine Learning process is used for detecting Memory based Anomalies to configure and tune JVM Parameters
  4. 4. #ATAGTR2020 As a author of this presentation I/we own the copyright and confirm the originality of the content. I/we allow Agile testing alliance to use the content for social media marketing, publishing it on ATA Blog or ATA social medial channels(Provided due credit is given to me/us) #ATAGTR2020 As a author of this presentation I/we own the copyright and confirm the originality of the content. I/we allow Agile testing alliance to use the content for social media marketing, publishing it on ATA Blog or ATA social medial channels(Provided due credit is given to me/us) Adaptive Learner In Performance Testing Lifecycle Identity NFR’s Design Work Load Mix Design Test Script and Scenario Run Test Using Load Testing Tools Analyse Test Results Design Build Testing Test Analysis Performance Tuning JVM using ML based Algorithm Re design Workload Mix /data Adaptive Learner Updated JVM Configuration
  5. 5. #ATAGTR2020 As a author of this presentation I/we own the copyright and confirm the originality of the content. I/we allow Agile testing alliance to use the content for social media marketing, publishing it on ATA Blog or ATA social medial channels(Provided due credit is given to me/us) #ATAGTR2020 As a author of this presentation I/we own the copyright and confirm the originality of the content. I/we allow Agile testing alliance to use the content for social media marketing, publishing it on ATA Blog or ATA social medial channels(Provided due credit is given to me/us) How It Works ? • Different test data sets are identified based full volume of data and predefined Scenario are created with the datasets for Load testing Identifying Data and Scenarios • Anomaly are identified in memory pattern and JVM Parameter which needs to be optimized are identified and tuned • Final testing is performed on correct dataset exercising the application methods effectively and running test against optimised JVM parameters Tuning JVM Parameters • The Adaptive Learner program analyse the datasets used and capture the relevant metrices like CPU, Heap Memory patterns from the applications server using APM tools. • Anomaly in memory usage patterns are analysed using machine learning based algorithm in the learner. • High utilization methods in the application program are identifies based on the usage of different datasets and based on which the rules are created as a feedback and test scenario is modifies for better performance load. Analysis using Adaptive Learner • Simulate Load testing with the subset of data using industry standard load testing tools like LoadRunner Performance Test Execution
  6. 6. #ATAGTR2020 As a author of this presentation I/we own the copyright and confirm the originality of the content. I/we allow Agile testing alliance to use the content for social media marketing, publishing it on ATA Blog or ATA social medial channels(Provided due credit is given to me/us) #ATAGTR2020 As a author of this presentation I/we own the copyright and confirm the originality of the content. I/we allow Agile testing alliance to use the content for social media marketing, publishing it on ATA Blog or ATA social medial channels(Provided due credit is given to me/us) Anomaly Detection An anomaly is referred to as any abnormal behaviour in application performance Various reasons for anomaly • Transaction Response time going beyond business approved SLA • High Utilization of CPU • Memory going beyond 75% of Threshold • Frequent occurrence of major Garbage collection The learner will concentrate on abnormality induced due to violation in memory consumption • Historical data samples are collated and stored as training data for ML Framework • The Framework is modelled to achieve higher success probability with the help of extensive training dataset • Output from the model serves as testing data for ML framework whenever an abnormality is detected in the application behaviour. • GC times are collected as test data using automated utility and fed into source database for ML Model. Data Collection for Machine Learning Framework
  7. 7. #ATAGTR2020 As a author of this presentation I/we own the copyright and confirm the originality of the content. I/we allow Agile testing alliance to use the content for social media marketing, publishing it on ATA Blog or ATA social medial channels(Provided due credit is given to me/us) #ATAGTR2020 As a author of this presentation I/we own the copyright and confirm the originality of the content. I/we allow Agile testing alliance to use the content for social media marketing, publishing it on ATA Blog or ATA social medial channels(Provided due credit is given to me/us) Determination of the Machine Learning Model Most suitable approach to achieve the relationship between the JVM metrics and the garbage collection time is to implement an Artificial Neural Network (ANN) using backpropagation algorithm An Artificial Neural Network is a mathematical structure which is widely used to determine a relationship between the input and output parameters. The aim is to find the optimal value of the weightage of the neural network to get the desired output What are the Steps involved in designing a back propagation algorithm ? • Defining Input Layer • Defining Hidden Layer • Defining Output Layer • Assigning Weight • Neuron Activation. • Neuron Transfer (Sigmoid Activation Function) • Forward Propagation • Transfer Derivative. • Error Backpropagation • Update Weights. • Train Network using Gradient decent Forward PropagateInitialize Network Back Propagate Error Train Network & Predict
  8. 8. #ATAGTR2020 As a author of this presentation I/we own the copyright and confirm the originality of the content. I/we allow Agile testing alliance to use the content for social media marketing, publishing it on ATA Blog or ATA social medial channels(Provided due credit is given to me/us) #ATAGTR2020 As a author of this presentation I/we own the copyright and confirm the originality of the content. I/we allow Agile testing alliance to use the content for social media marketing, publishing it on ATA Blog or ATA social medial channels(Provided due credit is given to me/us) Backpropagation using python The input layer for the ANN will consist of the most important JVM metrics and Load applied such as • Min Heap Size (m1) • Max Heap Size (m2) • New Size(m3) • New Ratio(m4) • Min Heap Free Ratio(m5) • Max Heap Free Ratio (m6) • Max New Size (m7) • Throughput(M8) • Output parameter is the predicted garbage collection time. • ML Framework will determine a relationship between the input and output layer by applying the training data to the ANN model. • The relationship can be represented as f(GCT) = f(m1, m2, .... , mn).
  9. 9. #ATAGTR2020 As a author of this presentation I/we own the copyright and confirm the originality of the content. I/we allow Agile testing alliance to use the content for social media marketing, publishing it on ATA Blog or ATA social medial channels(Provided due credit is given to me/us) #ATAGTR2020 As a author of this presentation I/we own the copyright and confirm the originality of the content. I/we allow Agile testing alliance to use the content for social media marketing, publishing it on ATA Blog or ATA social medial channels(Provided due credit is given to me/us) Key Benefits Application is Simulated with Different subset of data and load Identifying bottle neck in the system for specific data conditions Automated Feedback of system and MI based JVM Optimization improves application performance Tuning based on the Feedback helps in Optimising the system performance
  10. 10. #ATAGTR2020 As a author of this presentation I/we own the copyright and confirm the originality of the content. I/we allow Agile testing alliance to use the content for social media marketing, publishing it on ATA Blog or ATA social medial channels(Provided due credit is given to me/us) #ATAGTR2020 As a author of this presentation I/we own the copyright and confirm the originality of the content. I/we allow Agile testing alliance to use the content for social media marketing, publishing it on ATA Blog or ATA social medial channels(Provided due credit is given to me/us) Conclusion • Identify bottleneck • Identify methods which are heavily used base on different data sets • Automatic Feedback • 50% reduction in manual work • Identify Issue faster • Run different scenarios automatically • Optimise performance by tuning JVM parameters • Automated analysis of data shared by APM tools using ANN Algorithm
  11. 11. #ATAGTR2020 As a author of this presentation I/we own the copyright and confirm the originality of the content. I/we allow Agile testing alliance to use the content for social media marketing, publishing it on ATA Blog or ATA social medial channels(Provided due credit is given to me/us) #ATAGTR2020 As a author of this presentation I/we own the copyright and confirm the originality of the content. I/we allow Agile testing alliance to use the content for social media marketing, publishing it on ATA Blog or ATA social medial channels(Provided due credit is given to me/us)

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