Software testing is a critical aspect of the SDLC, but time and resource constraints can cause software companies to treat testing as an afterthought rather than the backbone of product quality.
The primary challenge in the field of testing is the lack of talent and expertise, especially in the automation testing, according to Nilesh Patel, senior director of software services at KMS Technology. Many organizations struggle with a lack of skilled testers capable of implementing and managing automated testing frameworks. As a result, companies are often looking outside to fill that gap and are increasingly turning to AI/ML.
Many organizations have some level of automation, but fail to take full advantage of it, resorting to manual testing, which limits their efficiency and effectiveness in identifying and resolving software issues, Patel added.
Another significant problem is the instability of test environments and inadequate test data. Organizations often struggle with unstable cloud setups or a lack of the necessary devices for comprehensive testing, hampering their ability to conduct effective and efficient tests. The challenge of securing realistic and sufficient test data further complicates the testing process.
A potential solution to this, said Patel of KMS, lies in using advanced technologies, such as artificial intelligence and machine learning, to predict and generate relevant test data, improving test coverage and reliability of test results.
Patel emphasized that applications are becoming more complex than ever before, so that AI/ML technologies they are not only essential to manage that complexity, but also play a key role in increasing test coverage by identifying gaps that might have been overlooked before.
“If you have GenAI or LLM models, they have algorithms that actually look at user actions and how users or end users use the application itself, and they can predict what data sets you need,” Patel told SD Times. “It also helps increase test coverage. AI can find flaws in your testing that you didn’t know about before.”
In an environment characterized by increased complexity, rapid release expectations and intense competition, with thousands of applications offering similar functionality, Patel emphasizes the critical importance of launching high-quality software to ensure user retention despite these challenges.
This challenge is particularly pronounced in the context of highly regulated industries such as banking and healthcare, where AI and ML technologies can offer significant advantages, not only by simplifying the development process but also by easing the extensive documentation requirements inherent in these sectors.
“The level of detail is through the roof and you have to plan a lot more. It’s not as easy as just saying ‘I’m testing it, it works, I take your word for it.’ No, you have to show proof and have buy-in and that’s it [applications] which are likely to have longer layoff cycles,” Patel said. “But that’s where you can use AI and GenAI again because those technologies will help discover patterns that your business can use.”
A system or tool can monitor and analyze user actions and interactions and predict potential defects. It highlights the vast amount of data available in compliance-driven industries that can be leveraged to improve product testing and coverage. By learning from every possible data point, including test case results, the algorithm improves its ability to provide more comprehensive coverage for subsequent releases.
Testing becomes everyone on deck
Multiple people in the organization are actively involved in testing to make sure the app works for their part of the organization, Patel explained.
“I would say everyone is involved now. It used to be just the QA team or the testing team or maybe some of the software developers involved in testing, but now I see it with everyone. Everyone must have high quality products. Even the sales team, they do demos right for their customers, and it has to work, so they have an opinion on the quality and in that case they even serve as your end users,” said Patel.
“Then when they sell, they get real feedback on how the app works. When you see how it works or how they use it, testers can take that information and generate test cases based on it. So, hand in hand. It is everyone’s responsibility,” he added.
In the area of quality assurance, the emphasis is placed on the business workflow being thoroughly tested and harmonized with the actual experiences of end users. This approach emphasizes the importance of moving beyond isolated or isolated tests to embrace a comprehensive testing strategy that reflects real-world usage. Such a strategy highlights possible functional defects that may not be apparent when components are tested in isolation.
To achieve this, according to Patel, it is critical to incorporate feedback and observations from all stakeholders, including sales teams, end users and customers, into the testing process. This feedback should inform the creation of scenarios and test cases that accurately reflect user experiences and challenges.
In this way, quality assurance can confirm the effectiveness and efficiency of business workflows, ensuring that the product not only meets, but exceeds the high standards expected by its users. This holistic approach to testing is key to identifying and resolving issues before they impact the user experience, ultimately leading to a more robust and reliable product.