How AI Is Transforming Performance Testing
Performance testing has always been a cornerstone of software quality engineering. Yet, in today’s world of distributed microservices, unpredictable user behaviour, and global-scale cloud environments, traditional performance testing methods are struggling to keep up. Enter Artificial Intelligence (AI) — not as another industry buzzword, but as a real enabler of smarter, faster, and more predictive performance testing. Why Traditional Performance Testing Is No Longer Enough Modern systems are complex, elastic, and constantly evolving. Key challenges include: Microservices-based architectures Cloud-native and containerized deployments Dynamic scaling and highly event-driven systems Rapidly shifting user patterns This complexity introduces variability in metrics and results: Bursty traffic and nonlinear workloads Frequent resource pattern shifts Hidden performance bottlenecks deep within distributed components Traditional tools depend on fixed test scripts and manual bottleneck identification, which are slower, reactive, and often incomplete. When systems behave in unscripted ways, AI-driven performance testing offers adaptability and foresight. How AI Elevates Performance Testing AI enhances performance testing in five major dimensions: 1.AI-Driven Workload Modelling Instead of guessing load patterns, AI learns real-world user behaviours from production data: Detects actual peak-hour usage patterns Classifies user journeys dynamically Generates synthetic workloads that mirror true behaviour Results: More realistic test coverage Better scalability predictions Improved reliability for production scenarios Example: Instead of a generic “add 100 users per minute” approach, AI can simulate lunch-hour bursts or regional traffic spikes with precision. Intelligent Anomaly Detection AI systems can automatically detect performance deviations by learning what "normal" looks like. Key techniques: Unsupervised learning (Isolation Forest, DBSCAN) Deep learning models (LSTMs, Autoencoders) Real-time correlation with upstream metrics prioritized, actionable recommendations and code-fix suggestions aligned with best practices Example: An AI model can flag a microservice’s 5% latency spike — even when it recurs every 18 minutes — long before a human would notice. Predictive Performance Modelling AI enables you to anticipate performance issues before load tests reveal them. Capabilities: Forecasting resource saturation points Estimating optimal concurrency limits Running “what-if” simulations with ML or reinforcement learning Example: AI predicts system failure thresholds (e.g., CPU maxing out at 22K concurrent users) before that load is ever applied. AI-Powered Root-Cause Analysis When performance degrades, finding the “why” can be challenging. AI shortens this phase by: Mapping cross-service dependencies Correlating metrics and logs automatically Highlighting the most probable root causes Example: AI uncovers that a spike in Service D was due to cache misses in Service B — a connection buried across multiple log streams. Automated Insights and Reporting With the help of Large Language Models (LLMs) like ChatGPT or open-source equivalents: Summarize long performance reports Suggest optimization strategies Highlight anomalies automatically within dashboards This enables faster, data-driven decision-making across engineering and management teams. The Difference Between AIOps and AI-Driven Performance Testing Aspect AIOps AI-Enhanced Performance Testing Primary Focus IT operations automation Performance engineering Objective Detect and resolve incidents Predict and optimize system behaviour Data Sources Logs, infrastructure metrics Testing results, workload data Outcome Self-healing IT systems Pre-validated, performance-optimized code before release Key takeaway: AIOps acts in production; AI-driven testing acts pre-production. Real Tools Adopting AI in Performance Testing Category Tools Capabilities Performance Testing Tools JMeter, LoadRunner, Neoload, Locust (ML Plugins), k6 (AI extensions) Intelligent test design, smart correlation, anomaly detection AIOps & Observability Platforms Dynatrace (Davis AI), New Relic AI, Datadog Watchdog, Elastic ML Metric correlation, predictive analytics, auto-baselining These tools improve log analysis, metric correlation, predictive forecasting, and test script generation. Key Benefits of AI Integration ✅ Faster test design — Intelligent load generation automates script creation ✅ Proactive analytics — Predict failures before release ✅ Higher test accuracy — Real-world traffic reconstruction ✅ Reduced triage effort — Automated root-cause identification ✅ Great scalability — Run leaner, smarter tests Challenges and Key Considerations ⚠ Data quality — Poor or biased input leads to faulty AI insights ⚠ Overfitting — AI assumes repetitive patterns without variability ⚠ Opaque models — Black-box decisions can hinder trust ⚠ Skill gaps — Teams require ML understanding ⚠ Compute costs — ML training adds overhead A balanced adoption strategy mitigates these risks. Practical Roadmap: Implementing AI in Performance Testing Step 1: Capture High-Quality Data Logs, traces, metrics, and user journeys from real environments. Step 2: Select a Use Case Start small — e.g., anomaly detection or predictive capacity modelling. Step 3: Integrate AI-Ready Tools Adopt AI-enabled load testing and observability platforms. Step 4: Create Foundational Models Use Python ML, built-in analytics, or open-source tools to generate forecasts or regressions. Step 5: Automate in CI/CD Integrate AI-triggered insights into continuous testing pipelines. Step 6: Validate Continuously Always align AI predictions with real-world performance measurements. Future Outlook: The Next 5–10 Years AI will redefine performance testing as we know it: Fully autonomous test orchestration Self-healing systems that tune themselves dynamically Real-time feedback loops across CI/CD pipelines AI-powered capacity planning for cloud scalability Performance engineers will evolve from test executors to system intelligence strategists — interpreting, validating, and steering AI-driven insights. Final Thoughts AI is not replacing performance testing — it’s revolutionizing it. From smarter workload generation to advanced anomaly detection and predictive modelling, AI shifts testing from reactive validation to proactive optimization. Organizations that embrace AI-driven performance testing today will lead in speed, stability, and scalability tomorrow.Part 1 - Develop a VS Code Extension for Your Capstone Project
API Guardian - My Capstone Project As software and APIs evolve, developers encounter significant difficulties in maintaining and updating API endpoints. Breaking changes can lead to system instability, while outdated or unclear documentation makes maintenance less efficient. These challenges are further compounded by the time-consuming nature of updating dependencies and the tendency to prioritize new features over maintenance tasks. The absence of effective tools and processes to tackle these issues reduces overall productivity and developer efficiency. To address this, API Guardian was created as a Visual Studio Code extension that identifies API endpoints in a project and checks their functionality before deployment. This solution was developed to help developers save time spent fixing issues caused by breaking or non-breaking changes and to alleviate the difficulties in performing maintenance due to unclear or outdated documentation. Features and Capabilities This extension has 3 main features: Feature 1. Developers can decide if the extension will scan or skip specified files in the project. Press “Enter” to scan/skip all files. Type the file name (e.g., main.py) and press “Enter” to scan/skip a single file. Type file names with a delimiter (e.g., main.py | pythonFile.py) and press “Enter” to scan/skip multiple files. Feature 2. Custom hover messages when developers mouse over identified APIs This hover message will vary based on the status of the APIs. If the API returns a success status, the hover message will only show the completed API and its status. However, if an error occurs, the hover message will include this additional information: (1) API Name, (2) Official API Link, (3) Error Message, (4) Title of Recommended Fix and (5) Link to the Recommended Fix. Feature 3. Excel Report with Details of Identified APIs After all the identified APIs have been tested, an excel report will exported with the following information to allow developers to easily identify the APIs in the project. What Technology and Products does it involved? Building a Visual Studio Code extension and publishing it to the Visual Studio Marketplace involves a mix of technologies and tools. The project was initiated using the NPM package, generator-code, to set up a JavaScript project for developing the extension. All the extension's logic will be developed and managed within the "extension.js" file generated during the setup process. Once ready for deployment, we will package the extension using "vsce" to generate a ".vsix" file, which will then be used for deployment to the Visual Studio Code Marketplace. The deployment process involves requiring the user to create a publishing account and using tools like vsce to upload and manage the extension's version, updates, and metadata. As part of this process, you would need to create a Personal Access Token (PAT) from Azure DevOps. This token is used to verify your identity and authenticate the publishing tool, allowing you to securely upload your extension to the Visual Studio Marketplace. The PAT provides the necessary permissions for tasks such as version management, publishing new releases, and updating the extension metadata. What did I learn? Throughout this journey, I learned not just about the technical stack but also about the value of detailed project setup and secure publishing processes. While the technical steps can be challenging, they’re incredibly rewarding, and I’m excited to dive deeper into it moving forward. I’m looking forward to exploring how the extension can be further improved and enhanced. If you're interested in learning more about how my API guidance was built, keep an eye out for my next post! API Guardian https://marketplace.visualstudio.com/items?itemName=APIGuardian-vsc.api About the Authors Main Author - Ms Joy Cheng Yee Shing, BSc (Hon) Computing Science Academic Supervisor - Dr Peter Yau, Microsoft MVP
Test Automation and EasyRepro: 01 - Overview and Getting Started
Learn in detail how to use the EasyRepro framework to do automated UI tests of Dynamics 365. You can use it to automate testing such as Smoke, Regression, Load, etc. The framework is built from the Open Source Selenium web drivers used by the industry across a wide range of projects and applications. This article is to walk through the setup of the EasyRepro framework and works with Unit Tests in Visual Studio and GitHub repositories.Managing Workflows with the GitHub Actions Extension for VS Code
GitHub Actions is a powerful feature that enables developers to automate their workflow and save valuable time. With the new GitHub Actions Extension for VS Code, developers have even more flexibility and power at their fingertips. The addon allows developers to design, amend, and run workflows without ever leaving the editor. Features include managing workflows, monitoring workflow runs, and workflow authoring. Syntax highlighting, integrated documentation, and validation and code completion for GitHub Actions Expressions and the YAML schema make editing workflows a breeze. Install the GitHub Actions extension for VS Code and start automating your development workflow today.
