Advanced Quality Assurance in AdTech

Introduction

In our last post, we discussed six key points of AdTech QA. Beyond these, for large-scale systems, we recommend Chaos Engineering, Fuzz Testing, Continuous Integration, Continuous Performance Measurement, and Testing in Production. These techniques are specifically designed to address the unique challenges posed by AdTech platforms.

What Differentiates AdTech Testing from Other Domains?

Real-Time Bidding and High-Frequency Transactions

• Latency Sensitivity: AdTech platforms operate in real-time, with bids and ad placements happening in milliseconds. Testing must ensure that these processes are fast and efficient.
• Volume and Frequency: The high volume of transactions requires robust performance and load testing to ensure the platform can handle peak traffic without degradation.

Diverse and Complex Integrations

• Third-Party Systems: AdTech platforms integrate with various third-party services such as DSPs, SSPs, data providers, and ad exchanges. Ensuring these integrations work seamlessly is crucial.
• API Testing: The behavior of APIs and data exchanges can vary, requiring thorough testing to verify consistent and accurate interactions.

Dynamic and Contextual Content

• Ad Rendering: Ensuring that ads render correctly across a wide range of devices, browsers, and screen sizes is more complex than standard web content testing.
• Dynamic Content: Ads are often personalized and contextually targeted, necessitating testing of various ad creatives and scenarios.

Advanced QA Techniques in AdTech

To meet the technical challenges posed by AdTech, QA teams employ a range of advanced techniques and methodologies:

• Chaos Engineering: Chaos Engineering involves deliberately injecting faults and failures into production systems to assess their resilience and recoverability. In AdTech, Chaos Engineering helps identify and mitigate single points of failure, resource contention issues, and performance bottlenecks in distributed ad-serving infrastructures.
• Continuous Integration and Deployment (CI/CD): CI/CD pipelines automate the build, testing, and deployment of ad-serving applications, enabling rapid iteration and delivery of new features. QA engineers design comprehensive test suites, including unit, integration, and end-to-end tests, to validate each code change and ensure seamless integration with existing systems.
• Fuzz Testing: Fuzz Testing, or fuzzing, involves sending invalid, unexpected, or random inputs to software applications to uncover vulnerabilities and edge cases. In AdTech, Fuzz Testing helps identify security vulnerabilities, protocol parsing errors, and interoperability issues in ad servers, software development kits (SDKs), and network protocols.
• Performance Profiling: Performance profiling tools, such as JProfiler, YourKit, and VisualVM, enable QA teams to analyze the runtime behavior and resource utilization of AdTech applications. By profiling CPU usage, memory consumption, and network activity, QA engineers can identify performance hotspots, memory leaks, and optimization opportunities for improving ad-serving efficiency.

Importance of Testing in Production in AdTech

Testing in production is not typically considered an advanced testing technique or methodology. Rather, it is a risky practice that is generally avoided in standard software development and deployment processes. Here are some reasons and contexts for testing in production in AdTech:

Real-World Conditions

• True Environment: Production environments provide the most accurate representation of how the system behaves under real-world conditions, including actual user interactions and traffic patterns.
• Live Data: Testing in production ensures that the system handles real data correctly, which can be crucial for detecting issues that might not be apparent in a staging or testing environment.

Performance and Scalability

• High Traffic: Production environments experience the highest levels of traffic, making them the best place to test performance and scalability.
• Load Handling: It allows for the evaluation of how the system handles peak loads and stress conditions, which is essential for maintaining ad delivery and performance.

Revenue Impact

• Revenue Assurance: Production testing helps identify issues that could affect revenue generation, such as improper ad serving, tracking issues, or incorrect bidding processes.
• Campaign Performance: Ensures that ad campaigns are running effectively and achieving the desired performance metrics.

Monitoring and Alerts

• Real-Time Monitoring: Enables the setup and verification of real-time monitoring and alerting systems to catch and respond to issues as they occur.
• Immediate Feedback: Provides immediate feedback on the health and performance of the AdTech platform, facilitating quicker issue resolution.

Conclusion

By leveraging advanced QA techniques like Chaos Engineering, Fuzz Testing, Continuous Integration, Continuous Performance Measurement, and Testing in Production, AdTech platforms can effectively navigate the unique challenges of the industry. These practices ensure that the high demands of real-time bidding, complex integrations, and dynamic content delivery are met with precision.

Authors: Lile Oniani, Narek Gasparyan