Artificial Intelligence-based testing can offer several benefits to testers in the form of enabling automation in writing test cases, improving test coverage, making predictive analysis, and identifying bugs early, and saving significant costs thereon.
Quality testing has become a critical factor in ensuring that a software application delivers customer satisfaction. It involves assessing and analyzing the software application under certain conditions to know its threshold and attendant risks when implemented. However, with software build life cycles turning more complex and delivery turnarounds reducing, QA testers need to identify any inherent glitches in the application quickly and effectively. Thereupon, the information is passed on to the development team to fix the glitches and turn the application worthy of being delivered or implemented.
The complexity of any software application under development has become more challenging due to the focus on quick releases. So, what used to take a month for delivery has become a weekly affair with updates factored in almost on a daily basis. This has made the job of software testers all the more challenging and has created conditions to make testing smarter, efficient, accurate, and predictive. No wonder AI-based testing has become the go-to area for testers to implement a continuous automated and precision-based testing process. In fact, AI testing has begun to play a critical role in quality control for a host of business and industry verticals. It helps to set in motion a slew of measures that is predictive, accurate, and objective. By freeing up human effort, AI-led quality assurance speeds up the SDLC to achieve outcomes as mandated by Agile and DevOps methodologies.
Further, given that human testers have subjective biases, especially in a manual testing setup where the quality of testing can change according to the quality of the testers, Artificial Intelligence app testing delivers on parameters like speed, cost, accuracy, performance, safety, and scalability, seamlessly and effectively. AI can not only find glitches in the software application but perform root cause analysis to understand the source of such glitches.
Benefits of using AI in QA testing
Since orchestrating test automation across CI/CD pipelines has become challenging, AI testing services are used to enable smart testing in applications that have multiple dependencies, resources, and technologies. The benefits of using AI-based testing are as follows:
Automation of writing test cases: One of the challenges QA testers face while conducting test automation is writing test cases. They often end up writing large test cases to detect a minor bug thereby impacting test efficiency. AI test automation can ensure the writing of precise test cases quickly and accurately. Besides, when developers/testers write test cases, instead of choosing the most efficient option – the one that generates the least redundant data, they write test cases that they are comfortable with. AI, on the other hand, can choose the most efficient test option where there would not be bottlenecks, manual involvement, or redundant data.
Improve overall test coverage: AI-driven testing can expand the scope of testing by looking into the memory and file contents, data tables, or internal program states. This way it can determine if the software application is behaving as expected. AI can execute several test cases in every test run, which is not possible with manual testing.
Identify bugs early and deliver cost savings: Bugs or glitches can be very expensive to fix if identified later in the SDLC. However, AI test automation can offer instant feedback to the developers about the presence of bugs and deliver significant cost savings. It is important to remember that the cost of fixing bugs after product release can be four to five times expensive than the ones identified in the SDLC.
Predictive analysis: Artificial Intelligence can analyze the existing customer data to predict how customers’ browsing habits and needs would evolve. This helps developers and testers to be a step ahead of users’ choices and offer quality products or services in alignment with their expectations. With AI-ML testing, the test platforms get better at analyzing user behavior and making precise predictions.
Conclusion
In a day and age where going digital is the ultimate goal for enterprises to reach out to their customers and be competitive, AI-powered platforms have become a tangible reality. AI-based testing helps to optimize test automation and let the tests to self-heal and execute. It helps in automating more areas of testing such as UI testing and visual validation. By analyzing large volumes of data, Artificial Intelligence can create comprehensive and precise test cases to validate the smallest bugs in the system.
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