Software Quality Assurance Analysts and Testers

Develop and execute software tests to identify software problems and their causes. Test system modifications to prepare for implementation. Document software and application defects using a bug tracking system and report defects to software or web developers. Create and maintain databases of known defects. May participate in software design reviews to provide input on functional requirements, operational characteristics, product designs, and schedules.

The occupation "Software Quality Assurance Analysts and Testers" has an automation risk of 51.7%, only slightly below its base risk of 52.5%. This figure reflects a balanced level of exposure to automation, largely influenced by the repetitive and well-defined nature of many QA and testing functions. Advances in AI and automation technologies have made it increasingly feasible for machines to identify, analyze, and document software problems. For instance, automated testing tools can efficiently detect program function issues, capture defects in bug tracking systems, and even generate comprehensive reports for developers. Tasks such as developing and running tests on databases, simulating various test scenarios (including regression, negative, and usability tests), are now frequently handled by sophisticated automation suites, further increasing the automatable component of this occupation. However, certain aspects of the role remain more resistant to automation due to their reliance on nuanced human judgment and experience. For example, storing, retrieving, and manipulating data for complex analyses of system capabilities and requirements often demand adaptive thinking and domain-specific expertise. Recommending new equipment purchases based on environmental fit or future-proofing involves understanding business needs and environmental variables in ways that automation cannot yet replicate reliably. Furthermore, modifying existing software—whether to fix bugs, improve performance, or adapt to new hardware—requires creativity and a deep understanding of both hardware and software interactions, underscoring tasks less susceptible to full automation. The bottleneck skills inhibiting complete automation in this field primarily revolve around originality, with a measured level of 3.0%. Originality encompasses ideation, creative problem-solving, and the design of novel solutions to unexpected issues—abilities that current automated systems perform poorly compared to humans. As the occupation becomes more dependent on tools that automate routine tasks, the importance of originality grows, creating a bottleneck that constrains further automation. This reliance on creative and adaptive thinking means that, while many procedural aspects of software testing are easy targets for automation, sophisticated human oversight and inventive troubleshooting will continue to play a crucial role in ensuring software quality.