AI-driven testing changes everything for testing teams. These Best Practices ensure best outcomes. 

I’ve recently published a series of posts on Best Practices for different aspects of software QA in the age of AI-driven testing. This post serves as a portal to them.

Before listing the posts, it’s worth noting that everything has changed in software QA given generative AI-driven testing, especially as enabled by the Appvance IQ testing platform. That sea-change means that new practices are required to fully benefit from this magical new technology. However, when a new generation of technology leads to new practices like this, there tends to be some trial and error. Here’s the best news: you can avoid the error part by following these proven best practices. 

Best Practices for Software Testing with AI-Driven testing

  • 4 Best Practices for Test Automation with MFA: Multi-Factor Authentication is a vital security measure, but presents challenges for test automation. By adopting the best practices outlined in this blog post, you will strike a balance between the need for MFA and the productivity of test automation.
  • 5 Best Practices for Dev and QA Collaboration: The collaboration between Dev and QA teams is crucial for successful test automation. By following the five best practices listed above, organizations can create a harmonious working environment where both teams work together to ensure fast release cycles and high quality. 
  • 6 Best Practices for Test Design with AI-driven testing: AI-driven testing presents transformative opportunities to enhance software quality and the processes around software quality. By rethinking the role of test scripts, establishing reporting rules, and evolving test case development and coverage strategies, organizations can optimize their testing efforts and quality outcomes.
  • Pros & Cons of Using Production and Generated Data for Software Testing: While using production data can be a tempting choice due to its expedience and realism, it comes with significant challenges. Anonymization of sensitive data and selecting relevant subsets are crucial steps to ensure data integrity and privacy, albeit the use of production data remains prone to failure. Instead, a well-designed and properly generated test data set is essential for identifying and resolving issues in software applications without compromising user privacy or data accuracy.
  • 6 Techniques that Minimize Load Testing Costs: Load testing needn’t drain your resources. By implementing these six best practices, you can ensure effective load testing that aligns with your development schedule and budget constraints.

For a complete resource on all things Generative AI, read our blog “What is Generative AI in Software Testing.”

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