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By Khurram Mir
Software testing has always been an essential step to developing new products and services, however, to keep up with the rising demand for high quality products, companies often have to spend a significant amount of time and money to test their products before going to market.
Considering how tedious it can be to write testing scripts and perform everything manually, it often took companies months and even years before their product was ready. That is why many manufacturers and software developers were so quick to adopt generative AI for their software testing process. In this article, you will learn more about how Gen AI has changed the software testing industry over the past 18 months.
How Software Testing Originally Started
In the incipient stages of software development, the testing process involved a fair amount of manual work. Cases would be predefined, and testers would execute the same ones, with the occasional changes, looking for cracks in the code. While this method was thorough, it was time-consuming, and the risk of human error was very high.
Automated testing made matters easier, as repetitive tests could be performed much faster. Different automated testing tools were a normality in software development companies, helping testers speed up the process. The problem was that even this was not enough; human intervention was required to a significant extent, especially in the more complex testing processes.
This is where generative AI came into the scene, adding the element of intelligence within. Based on learned patterns, it could “generate” entire applications based on an existing dataset. This could now create and run repetitive or complex tests and automatically modify them to adapt to the current order.
How Generative AI Improved Software Testing
Generative AI has brought numerous benefits when it comes to quality assurance due to its ability to resemble the human mind. Unlike automated processes, where humans still had to create every part of the test case first, generative AI could take this matter off their hands. This is how this action changed software testing:
1. Enhanced Test Coverage
Humans learn vast amounts of information throughout their lifetime, but the issue is that it takes the brain longer to process it. It’s also biologically made to erase information unless it has been continuously consolidated. For this reason, people can miss things in areas they have learned about but weren’t particularly focused on. This limited knowledge, along with potential mental fatigue, can cause them to not catch on to certain factors that could lead to error.
Generative AI does not have this issue and can process vast amounts of data within a few seconds. By using machine learning, it can identify relationships and patterns that otherwise could not be taught by human testers. They could catch the code areas that are more likely to contain long-term issues, allowing them to fix things early in the development process.
2. Identifying Complex Issues
Once more, while the human brain may have the experience to catch onto complex issues, it doesn’t always do that. Software programs have occasional non-linear behaviors and intricate interactions that could lead to vulnerabilities and unexpected bugs.
Humans can perceive those as normal, but generative AI models usually don’t. They can run many logs and codes within seconds, and when they stumble upon something potentially abnormal, they flag the issue. This way, the human part of the team can analyze and potentially correct the problem, leading to a more reliable piece of software.
3. Automating the Documentation Part
The automation of the test cases themselves has significantly reduced the time necessary to perform repetitive quality checks. The problem is that for these tests to do their job, they must be thoroughly worked on. Since every piece of software is unique in its own way, you’d have to go through countless technical books and different guides to put one meager test together. Depending on your resourcefulness, this can take precious days or even weeks.
Generative AI can automatically generate guides and annotations that can be used to back up your test case development. For instance, generative AI tools can write computer code based on the information they were trained in, sparing you the lengthy research process. All that you can do is adapt it to your case and make the appropriate changes here and there.
4. Adaptive Testing
Automation has been a game changer in the software testing process, but there is still one big problem: tests can eventually lose their relevance and accuracy. The orders can change with each code addition or modification, and the previously written test may no longer be good.
Generative AI, unlike your average automation process, is backed up by a pool of data. To top that up, it’s continuously learning with each command and addition to the database. This means that if the new test case has a slightly different aim, the AI system should pick up on that and make the necessary adjustments. This type of action can still be a hit-or-miss, depending on how well-trained the database is, but with the proper human intelligence assistance, it could take off a lot from the development process.
5. Dynamic Testing Opportunities
When testing models are created manually, they are done with a standard background. The developer had an environment in mind (or several of them), creating a realistic area to test it against. This can bring various limitations, depending on how many data sets you use.
However, Generative AI can create diverse models that the human brain could not have even thought about. Indeed, AI can tend to hallucinate when it does not have enough data, but even those scenarios can give you a couple of ideas. This significantly opens testing opportunities.
Generative AI Will Continue to Improve!
The age of AI is here to stay, and generative AI has already broken through and forever changed software testing. Used together with human intelligence, it can easily catch hidden bugs, improving the quality of a software product or service.
About the Author
Khurram Mir is the Chief Marketing Officer at Kualitatem, a software testing and cyber security company. The company recently released Kualitee, an AI software testing tool that provides real-time QA insights to test better, collaborate smarter, and release world-class software more efficiently than ever.