The AI Revolution in Software Quality Assurance: A New Era of Quality Engineering and Productivity
As technology advances at an exponential rate, the role of artificial intelligence (AI) in software quality assurance (SQA) has become increasingly prominent. From automating mundane tasks to enhancing overall efficiency and productivity, AI has proven itself as a powerful tool in the arsenal of QA teams. But what does the future hold? Can AI go beyond its current capabilities and take over even more significant roles, such as automatically generating and maintaining test scripts? Let's explore how AI is transforming SQA and what the future may hold.
The Current State of AI in SQA
AI is already making a significant impact on SQA processes. Maintaining test scripts has been wholly taken over (in many cases such as dynamic accessors) by AI and/or machine learning for the past several years. Self-healing tests and scripts are widely available now in major platforms (but not legacy open source), showing upwards of a 90% reduction in maintenance tasks. Today this is table stakes in a modern QA automation team.
One of the most notable areas where AI is being used is in supervised exploratory script generation, where the AI learns from past testing experiences and adapts its testing strategy accordingly, including validating outcomes. This can result in far more comprehensive application coverage and can help identify potential issues that would have been missed by manual or legacy automated testing.
AI has advanced the state of the art in the generation of test scripts. Traditionally, creating and maintaining test scripts has been a time-consuming and labor-intensive process. However, with the help of AI, this task can be greatly simplified. AI can learn test cases and flows and validations through a one-time (but critical) training session and automatically generate test scripts, significantly reducing the workload for QA teams, often by 10X or more. I have seen some validated cases with nearly 100X productivity improvement as measured by both a reduction in labor and an increase in overall application coverage.
But perhaps the most significant impact of AI in SQA is its ability to provide greater visibility into the potential issues in any build. By automating tasks and providing real-time insights, AI can give teams a clearer understanding of their testing progress and help them identify potential issues earlier in the development cycle.
The Future of AI in SQA
Looking ahead, it's clear that AI will continue to play a leading role in SQA. In the next few years, we can expect to see even more advancements in AI-first testing, with the potential for AI to take on an even greater role in the generation and maintenance of test scripts.
One area where AI is going to have the biggest impact is in the conversion of English-written test cases directly into self-healing test scripts. Traditionally, this has been a time-consuming process for scripters and often requires the majority of effort in authoring and maintaining scripts. An application with thousands of test cases may require some 5000 man-hours of scripting. But with AI-first technology on the way, it will be fully automated and wickedly fast. Few humans are required. Early alpha-level results show 1000s of scripts generated from exact test cases in a matter of minutes. 1000s of times faster than any method of legacy script authoring. Ultimately when available, this will shift a teams focus off of scripting and maintenance chores and allow QA teams to focus their efforts on more critical tasks, such as test data creation and prioritizing bugs.
Implications for the SQA Industry
As AI continues to evolve, the QA industry will need to adapt. This means that QA professionals will need to develop new skills and embrace AI-first technologies and processes. For 35 years many QA engineers have been focused on writing scripts. But it’s likely that task will disappear entirely in the coming years with advances in Natural Language Processing (NLP), AI, and visual capable languages (which can naturally work with and recognize visual elements as well as regular accessors) becoming orders of magnitude more advanced than Selenium. Rather than being afraid of AI, QA professionals should see it as an opportunity to enhance their skill set and become experts in AI-first processes, as that is the future.
However, it's also essential to recognize that AI is not a one-size-fits-all solution. While AI can automate many tasks, there will still be a need for human oversight and intervention. QA teams will need to strike a balance between AI-driven testing and manual testing, ensuring that both are used where they are most effective—even if writing or recording scripts goes by the wayside.
Conclusion
For those using the state of the art platforms, AI has revolutionized the SQA industry, and the near future looks to be an even bigger game changer. By automating tasks, providing greater visibility, and streamlining processes, AI is already helping QA teams become more efficient and effective. As the technology continues to evolve, it's essential for QA professionals to embrace AI and develop the skills needed to thrive in this new era of testing.
