Software testing is a critical aspect of the SDLC, but constraints on time and resources can cause software companies to treat testing as an afterthought, rather than a linchpin in product quality.

The primary challenge in the field of testing is the scarcity of talent and expertise, particularly in automation testing, according to Nilesh Patel, Senior Director of Software Services at KMS Technology. Many organizations struggle due to a lack of skilled testers capable of implementing and managing automated testing frameworks. As a result, companies often seek external assistance to fill this gap and are increasingly turning to AI/ML. 

Many organizations possess some level of automation but fail to leverage it fully, resorting to manual testing, which limits their efficiency and effectiveness in identifying and addressing software issues, Patel added. 

Another significant issue is the instability of testing environments and inadequate test data. Organizations frequently encounter difficulties with unstable cloud setups or lack the necessary devices for comprehensive testing, which hampers their ability to conduct efficient and effective tests. The challenge of securing realistic and sufficient test data further complicates the testing process. 

The potential solution for this, KMS’s Patel said, lies in leveraging advanced technologies, such as AI and machine learning, to predict and generate relevant test data, improving test coverage and the reliability of testing outcomes. 

Patel emphasized that applications are becoming more intricate than ever before, so AI/ML technologies are not only essential for managing that complexity but also play a crucial role in enhancing testing coverage by identifying gaps that could have been previously overlooked. 

“If you have GenAI or LLM models, they have algorithms that are actually looking at user actions and how the customers or end users are using the application itself, and they can predict what data sets you need,” Patel told SD Times. “So it helps increase test coverage as well. The AI can find gaps in your testing that you didn’t know about before.”

In an environment characterized by heightened complexity, rapid release expectations, and intense competition, with thousands of applications offering similar functionalities, 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 like banking and health care, where AI and ML technologies can offer significant advantages, not only by streamlining the development process but also by facilitating the extensive documentation requirements inherent to 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’ll take your word for it.’ No, you have to show evidence and have the buy-ins and it’s those [applications] that will probably have longer release cycles,” Patel said. “But that’s where you can use AI and GenAI again because those technologies will help figure out patterns that your business can use.”

The system or tool can monitor and analyze user actions and interactions, and predict potential defects. It emphasizes the vast amount of data available in compliance-driven industries, which can be leveraged to improve product testing and coverage. By learning from every possible data point, including the outcomes of test cases, the algorithm enhances its ability to ensure more comprehensive coverage for subsequent releases.

Testing is becoming all hands on deck

More people in the organization are actively engaged in testing to make sure that the application works for their part of the organization, Patel explained. 

“I would say everyone is involved now. In the old days, it used to be just the quality team or the testing team or maybe some of the software developers involved in testing, but I see it from everyone now. Everyone has to have high-quality products. Even the sales team, they’re doing demos right to their clients, and it has to work, so they have opinions on quality and in that case even serve as your  end users,” Patel said.

“Then when they’re selling, they’re getting actual feedback on how the app works. When you see how it works, or how they’re using it, the testers can take that information and generate test cases based on that. So it’s hand in hand. It’s everyone’s responsibility,” he added. 

In the realm of quality assurance, the emphasis is placed on ensuring that business workflows are thoroughly tested and aligned with the end users’ actual experiences. This approach underscores the importance of moving beyond isolated or siloed tests to embrace a comprehensive testing strategy that mirrors real-world usage. Such a strategy highlights potential gaps in functionality that might not be apparent when testing components in isolation. 

To achieve this, according to Patel, it’s crucial 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 the users’ experiences and challenges. 

By doing so, quality assurance can validate 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 essential for identifying and addressing issues before they affect the customer experience, ultimately leading to a more robust and reliable product.