Challenges of unit testing in cancer diagnosis software development
Developing medical device software presents unparalleled challenges, especially when meeting stringent Food and Drug Administration (FDA) and European Medicines Agency (EMA) regulatory requirements. These challenges include managing potential risks, thorough verification processes, and comprehensive record-keeping throughout the software development lifecycle.
Comprehensive unit testing is essential, but traditional manual methods are often time-consuming and resource-intensive. This was the challenge for a leading software company focused on innovative cancer diagnosis solutions. To stay competitive and meet regulatory requirements, speeding up their medical device software development became crucial to their success.
To address these challenges, the software company turned to Diffblue Cover—a powerful AI solution that automatically generates unit tests, including those for edge cases and complex scenarios. This approach not only greatly expands test coverage but also significantly reduces the time and effort required compared to manual testing.
Tackling unit testing with help from artificial intelligence
To achieve regulatory compliance, the CTO leading software development team realized they needed increased unit test coverage to meet current medical device software standards. This required quickly generating a substantially larger number of unit tests for their backend code. Automation and artificial intelligence (AI) provided the solution.
They faced several challenges that underscored the need for a robust unit testing solution. Low initial test coverage, particularly in legacy applications, made it difficult to ensure software quality and meet standards. The pressure was heightened by the stringent regulatory requirements of the medical device industry, which demand high levels of test coverage — something their CTO took very seriously.
Data logging and interpretation were a crucial need
Along with these external pressures, the organization realized that effective troubleshooting and test quality improvement relied on the development team’s ability to understand and analyze tool-generated logs. This highlighted the need for robust logging capabilities and the skills to interpret the data effectively. Additionally, it became clear that more comprehensive documentation was necessary, as incomplete or unclear records could impede the effective use of testing tools.
Legacy technology slowed development down
Finally, their legacy systems, not designed with modern unit testing practices in mind, limited the SaMD organization’s ability to accelerate development and stay competitive. The team recognized that manual testing alone was too time-consuming to meet project deadlines and regulatory demands, highlighting the critical need for automation tools to speed up the testing process.