AI-based coding and refactoring, along with intelligent code quality management systems, are clearly powerful tools that dramatically increase development speed. However, no matter how quickly code is written and improved, if the “quality” of the software ultimately delivered to users is not guaranteed, all efforts will be meaningless.
Today’s software development environment demands both shorter release cycles and higher quality levels. The proliferation of microservices and cloud-native environments has increased complexity, and traditional manual testing and script-based automation methods are unable to keep pace with these changes and are revealing their limitations. High maintenance costs, limited test coverage, and feedback delays continue to be major bottlenecks in development productivity.
This chapter focuses on AI-based quality assurance and testing. AI, especially LLMs, is fundamentally changing the paradigm of quality assurance by bringing intelligence and flexibility to repetitive and unpredictable testing areas.
In this chapter, we will diagnose the limitations of API testing, which is at the core of communication between services, and explore how LLM-based TestLAB AI alleviates the maintenance burden of API testing, enhances coverage, and implements agile feedback loops through specific examples. We will also discuss how AI solves the challenges of complex UI test automation in the super app era and how DeviceFarm QA agents “report, think, and execute” to enhance the resilience and efficiency of UI testing.