With immense competition in the software industry, quality is something that makes a software stand out from the rest. Companies may create high-end software with the latest functionalities built into it. Still, if the software quality is not up to the standards, it will likely not succeed in the hostile market with cut-throat competition. Along with the evolution of software development methodologies, their testing strategies have also grown in leaps and bounds. Testers have become more adept at not just catching the functional bugs but also improving the overall quality of the product with respect to its usage and performance.
While human creativity and empathy have always been irreplaceable qualities in traditional manual testing, the growing complexities of the software demand more from testing. Automated testing was one leap above manual testing, which fulfilled its role up to a certain extent, yet it was still not the final answer. Testers depended heavily on writing scripts in programming languages, which was an overhead for them. Enter Generative AI in testing, the final solution everyone was looking for. It came as the final one-stop solution for all the testing needs and challenges that were lingering for a long time.
In this article, we will explore the role of Generative AI in testing, its benefits, and how it is becoming an indispensable testing solution in the modern age.
What is generative AI in testing?
Automation testing used to be a widely adopted strategy for many years because of the immense benefits it offered. It reduced the overall time and effort needed for running repetitive test cases that were largely useful in the agile environment, where software goes through multiple iterations of development. Yet, one of the major drawbacks of it was that it still needed testers to write scripts in programming languages. The time they would save by automating repetitive tasks was compromised by the time they would spend on creating such test cases, well, not completely, but surely up to a certain amount. This is where Generative AI in testing proved to be useful.
In general, Generative AI is a feature of AI that uses large language models and deep learning algorithms to create new content. The content could be in the form of text, image, videos, code, or even test cases, which is what we are discussing here.
Implications of generative AI in testing
Unlike traditional AI, which was mostly used for classification and predictions, Generative AI creates new stuff. So, what it means for testing is that it can be used for:
- Generating new test cases: based on natural language commands, understanding product descriptions, or the background code.
- Creating synthetic test data: for simulating real-life scenarios to test a product without compromising privacy and affecting the daily operations of the product.
- Forecasting defects: by studying historical patterns of defects, the software development landscape, and system behaviours.
- Self-repairing test cases: whenever there’s a slight change in the code, the already created test cases can self-heal to stay relevant and functional without any human intervention needed.
All these features of Generative AI allow testing to become an autonomous and intelligent process.
Why is generative AI essential in testing?
The four features of Generative AI stated above, which are relevant to testing, are what make it most essential in a modern testing environment. Let’s expand them now to understand in detail.
Test case creation
Creating test cases was always a manual task in both manual and automated testing so far. This is one of the most resource-intensive tasks as it requires test scripts to be written in programming languages like Java, Python, etc. Also, in the cases where a software goes through frequent updates, creating test cases for every such iteration is a big hassle.
Generative AI’s approach
A generative AI testing tool takes care of complete test creation all by itself. All it needs is one of the following inputs to be able to understand and generate test cases: natural language commands, user stories, business requirements, or just exposure to the raw code. Basically, it understands the context within which your product must be tested and thereby creates expansive test cases with a wide coverage, leaving no gaps.
Synthetic data creation
Before releasing, it is essential to test your product against real-life scenarios. Obtaining the realistic test data is a challenge due to user privacy and compliance issues. Masking the data to make it anonymous, on the other hand, makes it unrealistic and may compromise the test quality.
Generative AI’s approach
Based on its study of the product, Generative AI creates synthetic data that almost looks real but contains no personal information of the users. Apart from generating the regular data for positive testing, it also creates rare and unusual data for testing edge cases, ensuring robust software testing.
Forecasting defects and debugging assistance
It is a known law in testing that the later you discover a bug, the more expensive it becomes to fix it. Traditional testing always falls short of this feature. Testers are usually so occupied with testing the prime objectives and functionalities of the software that predicting a future bug is something beyond their capabilities.
Generative AI’s approach
A Generative AI testing tool can analyze the historical patterns of defects, system logs, and code changes, based on which, it can predict the high-risk areas that need dedicated testing. It can notify users of the potential bugs that are most likely to occur in the future. Since it is intelligent enough to predict a bug, it can also provide the best solutions for fixing it. It acts as an assistant for testers, not just for catching bugs but also for fixing them.
Continuous adaptability and scalability
Modern software products are usually in a state of flux. The rising customer expectations, the frequent changes in the business logic, or the regulatory rules are a few of the prime factors that require regular updates in the software. Keeping the old test cases abreast with the frequent product changes is a bottleneck in maintenance.
Generative AI’s approach
Once a Generative AI-powered testing tool is integrated with the software, it keeps upgrading its test suites as per the changes in the product. A new feature included, a new rule added, or any other code changes in the application can be easily detected by the testing tool, and thus, it creates new test cases for them. No matter how many stages of evolution your product goes through, the test cases keep scaling with them.
Final thoughts
Whether your product belongs to the banking and finance sector, healthcare, or e-commerce, Generative AI testing will always be the best strategy to go for. It can generate synthetic test data for your financial app, execute load and performance testing for your online store, and simulate patient scenarios for identifying health issues. It’s a one-stop solution for all your testing needs.
Although it is one of the best inventions in modern testing frameworks, it is still in its infancy. Over-reliance on AI may not always be a wise decision, as it may leave blind spots if human testers leave it unchecked. Companies can reap the most of this latest technology by collaborating its automation power with human intuition for a complete coverage of their testing process, and thereby, delivering a high-quality, robust product.
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