Artificial Intelligence (AI) technology is creating a huge buzz and considered to be the future technology. It has revolutionized all industries by utilizing intelligent algorithms to train the models and perform with logic and accuracy. AI has made its presence in software testing too and has improved the quality assurance process by multiple folds. It has increased productivity, consistency, accuracy, and agility while ensuring reduced time, cost, and effort considerably.
Though the issues in software quality improvement were resolved significantly by codeless test automation tools, speed was still a concern. AI-integrated test automation tools are proven to improve speed like never before and reduce human interference almost completely. Research is being conducted to further improve AI technology to leverage the best out of it.
Benefits of AI-powered test automation
The trend of AI-integrated test automation is still at a nascent stage yet has managed to change the process drastically. The scope of AI and ML in test automation is vast and beyond one’s imagination. These latest technologies have opened new doors of opportunities for testers and made way for new techniques to improve product quality like never before.
AI testing models are gaining popularity for the greater number of benefits they offer. It is predicted that AI-powered test automation models will replace other models in the near future. Here are some of the key benefits of AI test automation:
Improved accuracy: AI testing can improve the accuracy of the process to nearly 100% and automate mundane tasks to reduce human errors.
Higher test coverage: It has been statistically found that the AI-testing process has the capability to perform tests at deeper levels compared to other automated testing processes/tools. It improves the scope of test coverage multiple folds and improves the software quality significantly.
Saves time and money: The intelligent models carry out testing with great speed, hence, reducing time. Since the testing is performed quickly, the cost of additional resources is also cut down, thereby, reducing the cost of the process too.
Regression testing: Conducting regression testing is a daily affair. Organizations usually set a time and run regression testing every day to check for errors across the system. AI testing performs this tedious regression testing in lesser time than the other models.
Predictive analysis: The AI testing bots have the intelligence to analyze the existing customer data and define customer behavior in future. This trend can help companies to refresh their operational strategies based on predictive data analysis.
Improved defect tracking: AI-bug tracking is an accurate, automated, and highly consistent process. It identifies even the slightest error and helps the developer create a flawlessly working system.
Though AI testing is carried out with the common aim of creating an error-free software system, what makes them unique from manual and codeless testing is its intelligent approach. The machine learning capabilities allow them to analyze and perform the task. Their predictive approach is yet another add-on that keeps the organization future-proof.