Today’s demand for seamless user experiences on increasingly complex applications drives the need for software testing that can keep pace with this rapid technological advancement. It raises the question: should AI have a place in software testing? As I’ve discussed before, AI and natural language-based models like ChatGPT should be seen as merely another tool that we should be using as well-rounded software quality professionals. It requires intelligence on the part of the tester and does carry risk but on balance, AI is of benefit to what we can term ‘traditional testers.’
The rapidly increasing complexity of software and its role in delivering a competitive advantage while maintaining customer satisfaction provides an increasing challenge to ‘traditional testers’ in terms of efficiency. How will AI change the conventional way software testing uncovers potential issues before they reach end-users, and what will the implications of these changes be?
What is a traditional tester?
In the sphere of software development, a traditional tester is a professional responsible for conducting different types of tests to verify the quality of a software product. They plan test strategies, scrutinise the product or system, devise test cases, and conduct these tests to identify any potential defects, inefficiencies, or inconsistencies in the software. Their goal is to ensure the product aligns with the stated requirements and user expectations.
While tools and methodologies may evolve, the essence of a traditional tester's role is to uncover potential issues before they reach end-users. They utilise their knowledge, problem-solving skills, critical thinking, creativity, and comprehension of both the technology and business domains to perform this role effectively.
How will AI change software testing?
Plan: AI will enable smarter test planning by using algorithms to help in risk evaluation and deciding test flow.
For example, AI technologies like Testim's AI-based Smart Locators can identify the most frequently modified areas of the code or those sections where bugs have been recurrently found, providing invaluable insights for regression test planning.
Analyse: AI will enhance a tester's capacity to analyse both the product and the testing process. Predictive analytics powered by AI will help pinpoint potential problem areas in the software. Tools like Functionize use AI and machine learning to process testing data, identifying patterns and trends that may otherwise be missed by human testers.
Design: AI will revolutionise the design of test cases. AI can autonomously generate and optimise test cases based on system models. Technologies like TOSCA’s Vision AI can autonomously maintain these test cases, adjusting them to system updates without the need for manual intervention.
Execute: AI will significantly improve test execution. Tools like Appvance’s AI-driven testing technology can learn from previous test cycles to improve future test scripts, adapt to changes in the software, and reduce the time required to update test scripts.
The Future of AI-Powered Testing in Software Quality Assurance
The integration of AI in software testing will lead to several changes:
Skillset Adaptation: Testers will need to understand AI algorithms, machine learning models, and how to interpret their results. They will also need to become adept at using new AI-powered testing tools effectively.
Increased Efficiency: AI will handle repetitive and labour-intensive tasks with high efficiency. This will enable testers to focus more on exploratory testing, usability testing, and other areas where human judgement and creativity are crucial.
Change in Role: The role of testers will evolve from being ‘test executors’ to ‘test designers’ and ‘quality strategists.’ They may also take on the role of ‘AI trainers’, instructing AI systems on how to test more effectively.
Improved Quality: AI’s ability to analyse vast quantities of data and detect patterns will improve the overall quality of testing, leading to more reliable software products.
Ethical and Practical Challenges: The advent of AI in testing will introduce new ethical and practical issues, such as job displacement due to automation, or the need to ensure fairness, transparency, and accountability in AI-powered testing processes.
In conclusion, AI will not replace traditional testers but will bring about a transformation in their roles and responsibilities. The future of testing will see a blend of AI capabilities and human skills to deliver high-quality software in a more efficient and effective manner.