7 Impacts of AI-Driven Testing on Dev Teams

Most of our posts focus on the QA team, e.g., the Impact of AI on Test Teams from September. However, this one explores the impact of GenAI-driven testing on the development team. This is because GenAI has totally changed how QA teams pursue their mission, but has also significantly changed how dev teams interface with the quality function. These changes for the dev team start at the outset of the SDLC and carry all the way through to maintenance and regression testing. Here are seven impacts for the dev team to consider.

1. Quality Starts with Requirements

Quality software begins with well-defined and comprehensive requirements. Those documented requirements now take on a new role and new importance since they can serve as training fodder for GenAI-driven testing. For instance, a platform like Appvance Intelligent Quality (AIQ) can literally generate test plans from a Product Requirements Document (PRD) and later execute those tests against the resulting software.

Thus, it behooves the product manager or engineer who is creating the PRD to anticipate its use by a GenAI-driven testing platform like AIQ. Find more color on this topic in my recently posted Cheat Sheet: Getting Ready for AI-driven Test Projects.

2. Dev Must Use AI for Code Reviews

Code reviews are integral to maintaining code quality, but the manual review process can be time-consuming and impede the smooth flow of dev. Fortunately, AI-driven tools can largely automate code reviews. Machine learning algorithms can analyze code patterns, identify potential bugs, and even suggest improvements based on best practices. This not only accelerates the review process but also enhances the overall code quality, leading to more robust and maintainable software.

3. Dev Must Use AI for Test Data Generation

Effective testing requires diverse and realistic test data to simulate real-world scenarios. A GenAI-driven testing platform like AIQ generates meaningful test data by leveraging algorithms that understand the application’s behavior. This not only expedites the testing process but also ensures that the software is tested under a wide range of conditions, uncovering potential issues that might go unnoticed with static or manually created test data.

For more color, please refer to my recent post Pros & Cons of Using Production and Generated Data for Software Testing.

4. Dev Should Consistently Label UI Elements

GenAI-driven testing gets below the surface of the user interface (UI). This white-box testing is greatly aided by consistent labeling of UI elements. When developers do this, they make it easy for the GenAI-created tests to consistently and comprehensively test functionality, notwithstanding cosmetic changes to the UI.

5. Triage of Discovered Defects Must Be On Point

GenAI-driven testing significantly increases the volume of detected defects, making efficient triage paramount to a smoothly running SDLC. Development teams must implement robust processes for prioritizing and handling defects, especially given a profusion of newly discovered low-pri defects. While such triage is standard operating procedure for any competent development team, it becomes all the more critical given the large increase in detected defects found by GenAI-driven testing. Done right, this ensures that critical issues are addressed swiftly, maintaining a healthy development pace and preventing bottlenecks in the software delivery pipeline.

6. QA’s Job is Assurance of Quality, not Bug Detection

With GenAI-driven testing taking the lead in bug detection, the role of QA evolves. In particular, QA teams should shift their focus from routine bug detection to more strategic activities, such as devising comprehensive test strategies, exploring unconventional testing scenarios, and collaborating with development teams to enhance overall software quality. Dev teams should welcome this elevated role for QA.

7. Automated Regression Testing

One huge benefit of GenAI-driven testing is the automation of regression testing. Please refer to my recent Cheat Sheet for Regression Testing in the Age of GenAI Testing for details. For dev teams, automated regression testing is a security blanket that no unanticipated quality degradation will sneak in, even in the face of rapid release cycles.

Conclusion

The impact of AI-driven testing on development teams is major, albeit not as transformative as it is to QA. However, the benefit of GenAI-driven testing to dev teams is tremendous, including finding defects earlier in the SDLC, automating regression testing and better documentation of defects, to name a few particulars. By following the above tips, dev teams can seize those benefits for themselves.

Recent Blog Posts

Read Other Recent Articles

In the fast-evolving realm of technology, software testing is no longer a mere quality assurance process but a dynamic and multifaceted discipline that incorporates AI and autonomous systems. These technologies are increasingly revolutionizing the way we approach software quality. Let’s delve into the implications and future possibilities of AI and autonomous systems in testing and

In the fast-paced world of software development, ensuring the reliability and functionality of applications is paramount. Traditional methods of software testing rely on manually crafted test cases and data, which is time-consuming, expensive, and sometimes lacking in completeness. However, with the advent of Gen AI, there’s a paradigm shift in how test data is generated,

In the ever-evolving landscape of software development, ensuring the reliability and effectiveness of applications is paramount. As technology advances, so do the challenges in creating comprehensive test scenarios that mimic real-world conditions. One of the key components in achieving this is test data generation, and the integration of Gen AI is proving to be a

Empower Your Team. Unleash More Potential. See What AIQ Can Do For Your Business

footer cta image
footer cta image