Testing data is vital for many QA teams, particularly since most banks, insurance firms and financial services companies move towards more frequent and broader testing.
Moreover, a majority of large companies in the finance service are actively automating their testing procedures and practices.
Particularly larger players, who have deep pockets and large teams, are investing heavily in AI-powered tools and solutions.
However, some fierce obstacles remain, as data quality and availability are considered two barriers to using AI for software testing, according to Vijay Daniel, the CEO and founder of Simplify3X Software.
“Software testing is one area where artificial intelligence is advancing remarkably. With AI-powered automated functional and regression testing, opportunities for optimising manual testing methods and assuring software quality are nearly limitless,” Daniel shared.
Therefore, he called it “vital” to find and identify the “hidden advantages” of AI as more and more companies realise its potential for software testing.
Furthermore, for companies looking to improve their testing procedures, develop test automation tools, produce higher-quality software, and maintain their competitive edge in the modern business environment, he said it is critical to comprehend and capitalise on the potential of AI in software testing.
“Opportunities for optimising manual testing methods and assuring software quality are nearly limitless.”
– Vijay Daniel
Singling out a Mordor Intelligence study, Daniel said the size of the automation testing industry is anticipated to increase at a compound annual growth rate of 16.03%, from just under $28 billion last year to more than $58 billion by 2028.
However, he stressed it is just as critical to recognise and deal with the challenges of integrating AI into software testing procedures.
A main obstacle, according to Daniel, is insufficient data quality.
“Data quality and availability are two major barriers to using AI for software testing,” he stated.
“For AI models to function well and be trained, high-quality data are required. Such data collection and management can be challenging, particularly for advanced software systems,” Daniel added.
Furthermore, he highlighted that testers are frequently required to collect data on user interactions, system performance, and defect occurrences.
“It takes meticulous cleaning and precise data labelling to make sure the AI model learns the right patterns,” Daniel said.
Costs and complexity
And then there are costs and the complexity of it all.
“Integrating AI into software testing presents challenges both in complexity and cost, particularly in the initial stages,” Daniel wrote in a recent analysis.
Furthermore, he called training AI models “computationally intensive, often necessitating dedicated hardware and software resources.”
“Compounding these challenges is the need for testers to acquire proficiency in utilizing AI tools and methodologies effectively,” Daniel said.
Another challenge for banks and other financial services firms is insufficient domain expertise and flexibility.
“The data used to train AI models is crucial for their functionality.”
– Vijay Daniel
“The artificial intelligence system may find it difficult to come up with reliable findings if the training data does not fairly reflect the variety of scenarios and complexity of the software under test,” Daniel elaborated.
Furthermore, AI models may find it difficult to adjust to software environments that change quickly, necessitating frequent upgrades and retraining, he warned.
On the other hand, human testers possess the ability to promptly adjust to novel circumstances and utilise their proficiency to manage unexpected scenarios.
“Interfaces between functionalities within module, interfaces across module and release notes with code component details captured will help AI model to discover on its own test execution plan for a given release,” he shared.
If these obstacles can be captured, the sky is the limit, Daniel believes.
“The introduction of artificial intelligence into software testing represents a revolutionary change, providing improved accuracy, effectiveness, and prediction skills,” he stressed.
“If the advantages of AI overcome the challenges associated with data quality and cost, they might completely transform software quality assurance,” Daniel concluded.
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