Extrapolation to Excellence: AI and ML Triumph in Performance Testing Metrics

By Balakrishna Godakhindi, Tech Lead, Fiserv and Bhagyashree Radhakrishna, Advisor, Global Services, Fiserv

In today’s fast evolving domain of software development, performance testing is crucial to deliver consistent and high-quality experiences for users. Performance testing ensures applications not only meet but exceed delivery requirements by simulating real-world scenarios, identifying hidden bottlenecks, and measuring response times. These qualities can facilitate scalability, enhance user experiences, and fortify reliability. To achieve all this in an effective manner, the blended deployment of Artificial Intelligence (AI) and Machine Learning (ML) in the field of performance testing has emerged as a beacon of progress.

Improving performance predictions by leveraging AI and ML
Gone are the days of extrapolating results from lower configurations to predict performance in the production environment, a method often riddled with inaccuracies. Extrapolating performance metrics from lower configuration environments to predict performance in production environments can be challenging due to the inherent differences between the two environments. Variations in infrastructure, network conditions, data volumes, and user behavior can significantly impact performance, making extrapolation unreliable and imprecise.

Today, AI and ML are the architects of precision, breathing life into performance predictions. Training ML models on historical data and incorporating AI algorithms provides more accurate performance predictions, thus enabling organizations to optimise their applications and infrastructure effectively.

Deploying AI and ML in a systematic and iterative approach
The journey of AI and ML in performance testing begins with a meticulous, iterative journey. At each step, it is important to understand the domain, define laser-focused objectives, and gather a representative dataset. The chosen model can then be deployed in production, continually monitored, and updated. This method combines domain knowledge, careful data preparation, thoughtful model choice, and ongoing refinement for accurate ML solutions.

When it comes to picking the best predictive algorithm, factors like the nature, complexity, and comprehensibility of the data, and the available resources should be considered. In application, two go-to algorithms are linear regression which works best in simple and easy-to-understand situations, and gradient boosting, which allows weaker models to be combined to achieve robustness and high accuracy. With the implementation of these algorithms, production performance metrics can be predicted more accurately by running performance tests in lower configuration environments.

Driving cost efficiencies through lower configurations
Cost efficiency emerges as a pivotal player at this point, as the extravagance of maintaining a production-like environment is curtailed by half. The lower configuration landscapes can shoulder the responsibility, rising swiftly, while serving their purpose. AI and ML-based performance prediction improves the accuracy of performance metrics in production environments compared to traditional extrapolation methods. It allows for efficient resource allocation, early detection of performance bottlenecks, and informed decision making for scalability.

The power of AI and ML can be leveraged for portfolio optimisation by utilising historical market trends and predictive analytics to fine tune investment portfolios and enhance investor success. Additionally, AI-driven fraud detection and prevention mechanisms help identify anomalies and extrapolate potential fraud scenarios. This empowers organisations to proactively safeguard against fraudulent activities, thereby bolstering the security and trust in financial transactions.

In the realm of fintech, the horizon gleams with promise as AI and ML take center stage in performance testing. The integration of AI and ML technologies will enable enterprises to drive unparalleled user experiences, alongside reliability and high efficiency standards.