Impact of GenAI on the Software Testing Market

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Being a Chief AI Officer, one key responsibility that comes with my role is to ensure that AI is being rightly used to solve business problems that warrant its use. Most of my conversations these days naturally evolve to the uses of generative AI (GenAI). Every business executive and technology leader is interested in understanding how they can leverage this revolutionary advancement in the AI landscape to accelerate their business growth.

In this article, I’ll give an overview of the increasing adoption of AI in testing, especially in test generation and maintenance.

Let’s get started with understanding how it is revolutionizing the software testing space.

Understanding the GenAI Software Testing Market

 
With a growing demand for “shift-left” testing approaches, the need to integrate tools early in the development cycle becomes inevitable. Furthermore, the regulatory pressure is right on track and is increasing by the day, making organizations put a stern focus on security testing and compliance.

Intelligent test automation platforms provide AI-powered capabilities for test creation, anomaly detection, and self-healing tests.

As this informative article does not focus on the key players, let’s focus on the key criteria that can help you choose one for your business needs.

While some vendors might offer seamless CI/CD integration with strong analytics and good scalability, factors like price point, user-friendliness, learning curve, customer support, and integration with other tools for complete testing provide a good lens to gauge their effectiveness.

GenAI on Software Testing

 
Now that we have an overview of the GenAI software testing market, let’s reason whether technology like GenAI deserves merit for software testing. Imagine if GenAI can automatically generate test cases and scripts, including unit tests, integration tests, and even some forms of end-to-end testing. Would that save you any time and effort?

If your answer is yes, then here is good news: technology is being put to the right use, wherein AI can analyze code and identify edge cases or scenarios that could lead to more comprehensive test suites and higher-quality software.

Next up is the Achilles heel of most human testers when they have to identify the potential cause of bugs. GenAI can take that effort away and assist in analyzing patterns and potential causes of bugs more quickly – be it from crash logs, error reports, or user feedback. Its prowess is not just limited to identifying the issues. It can also suggest potential fixes or workarounds based on similar issues in its training data.

Business and Technology Team Alignment

 
As I’ve progressed in my career, I have observed this one commonly overlooked elephant in the room — the gap between business and technology teams.

As is often the case, businesses present certain requirements to address a challenge and initiate discussions with the technology team. The technology team begins writing user stories based on their interpretation of the problem. But, often requirements get lost or misunderstood in translation. Therefore, by the time the solution reaches the business, it often doesn’t align with business expectations.

Have you also ever faced a similar situation?

To address such a gap, GenAI can interpret natural language requirements and user stories to generate relevant test scenarios. Here is how.

GenAI can automatically generate test scenarios directly from the business requirements written in the form of natural language. It ensures that the technology team correctly grasps the business ask. If you think of it, these test scenarios serve as a bridge between what the business wants and how the technology team understands it. In fact, such tests become a form of validation, ensuring that the requirements are correctly interpreted before the team starts with the development.

Intelligent Test Prioritization

 
AI is best at computing — that is, its ability to analyze large datasets which gives it an edge over human ability.

Based on historical test results and analysis of code changes, AI can prioritize which tests should be run first or most frequently. Such prioritization can lead to more efficient use of testing resources and faster feedback cycles.

Speaking of feedback and iterations, GenAI systems can also learn from previous test runs and user behavior to continuously refine and improve test suites over time.

Trust with GenAI

 
As is common with all AI applications, ethical considerations do play a role. More so in software testing applications, where it is critical to ensure the reliability and interpretability of AI-generated tests. How do organizations build trust with this technology and ensure that it does not overlook critical test scenarios?

These are considerations that must be addressed moving forward.

Future Work

 
Following up on the ethical aspect, the fear of job displacement deserves a mention. Will GenAI software testers put the software testers’ roles at risk?

I have seen this development across the industry, which applies to experts in software testing too. Their role may evolve to focus more on test strategy, AI oversight, and complex scenario design. Quoting a popular phrasing, “AI will not take your job, but the human experts using AI will” — in our context, there may be increased demand for testers with AI and machine learning skills.

Lastly, the article won’t be complete if I do not touch upon the cost-benefit aspect. While the benefits are many, there is a cost involved. Even though GenAI could reduce some testing costs through automation, it brings its own costs in the form of AI tools, infrastructure, and expertise.

With all these factors in place and the fact that the GenAI technology is maturing with time, the software testing industry certainly has a promising future ahead.
 
 

Vidhi Chugh is an AI strategist and a digital transformation leader working at the intersection of product, sciences, and engineering to build scalable machine learning systems. She is an award-winning innovation leader, an author, and an international speaker. She is on a mission to democratize machine learning and break the jargon for everyone to be a part of this transformation.

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