In today’s business world, every second matters. How can you increase your team’s velocity and reduce cycle time in an agile environment? Automation has long been used for accelerating deployment and reducing overall product delivery cycle time in software development. Adopting generative AI (GenAI) in quality engineering (QE) is set to further accelerate and optimize operational efficiency.
Many companies use GenAI for specific tasks. But what if its application could be extended across an entire QE team? Testing typically involves understanding requirements, creating test cases, generating test data, creating regression test suites, and developing automation test scripts. Imagine a scenario where GenAI is utilized at every step of testing. This blog explores how GenAI can enhance QE by reducing dependencies and building highly efficient teams.
Understanding the Limitations of QE Today
Consider a project deploying new screens to an existing web portal. In a typical scrum team, new stories and epics are assigned to multiple agile teams consisting of expert developers and subject matter experts (SMEs). They test the functionality and deploy new screens without disrupting the flow.
From the start of the sprints, the QE team spends considerable time on avoidable tasks, such as creating testing tasks, uploading them to Jira, and breaking down tasks into subtasks. During sprints, they focus on creating test cases, identifying and generating test data, developing automation scripts, and other related activities.
How GenAI Can Optimize QE
Deploying a GenAI-enabled team to perform key tasks across the QE process can significantly enhance efficiency, beginning with the program increment planning meeting.
When the product owner introduces new user stories to the entire team, the QE team can use a GenAI tool (for example, ChatGPT) to quickly assess impacted areas based on existing functionality or test cases and create a test plan accordingly. This process enables quicker, high-quality analysis.
Throughout the sprint, the QE team can leverage GenAI tools to optimize and accelerate tasks, such as:
- Automatically generating test cases from user stories
- Creating test data based on test cases or user stories
- Developing automation scripts
- Conducting reviews to increase test coverage
- Analyzing the regression suite and keeping it up-to-date in real time by identifying impacted test scripts
With the use of GenAI in QE, teams must manage time differently from traditional teams that handle most of the activities manually.
Key Benefits of GenAI-enabled Teams
- Increased velocity
- Standardized and accurate story creation
- Faster impact analysis using trained large language model (LLM) data
- Reduced defects
- Faster code generation
- Improved code quality
- More accurate code reviews
- Better code documentation with minimal developer effort
Conclusion
GenAI adoption is no longer a luxury but a necessity for software engineering teams to deliver faster, highly accurate, and cost-effective results. In the future, GenAI will not just be a tool but an integral part of the entire software development lifecycle (SDLC), driving a new era of efficient QE.
