Original article was published on Artificial Intelligence on Medium
Artificial Intelligence and Its Impact on Software Testing
Can you imagine the efficiency and productivity of a system that is able to conduct a test and diagnose itself, and make self-corrections? It would lead to significant time and cost savings for the company. Artificial intelligence, today, is seeping into every aspect of the business and has been able to penetrate many industries, however, when it comes to software testing artificial intelligence can be a game-changer. A software testing services company that employs human resources for the testing process may still be valuable for the organizations, however, integration of software testing with artificial intelligence makes the entire procedure faster, easier, and more reliable.
The Need for AI in Software Testing
With the sophisticated tools and mechanisms of artificial intelligence at their disposal, testers are able to optimize the risk coverage, conduct inspection of the portfolio, minimize redundancies, detect the defects in the system automatically, diagnose false positives or negatives, and most importantly, analyze user experience. It is stated that almost 60 percent of the test cases that are available in the company’s portfolio are redundant; and with the help of artificial intelligence, software testing services companies these can be mitigated. Artificial intelligence uses its advanced tools and methods to identify test cases that are physically as well as logistically identical, and then eliminates the duplicate ones. In addition to this, artificial intelligence has the ability to maximize risk coverage and defect detection while minimizing the costs and the time used for execution. The integration of software testing with artificial intelligence creates a system that is capable of discovering weak nodes in the test case portfolios by tracking tests that are not linked to the requirements, flaky tests, untested requirements, and unused tests as well. Moreover, this kind of system has generally self-healing properties that allow it to mend the broken test cases, and make the test automation robust and resilient for any changes.
How does AI Integration Benefits Testers?
Even the most experienced and sophisticated quality assurance manager is bound to make mistakes when it comes to software testing; they might lose focus or overlook an important but hidden defect in the system owing to the extensive data. However, with artificial intelligence, the accuracy of the testing process is enhanced as it eliminated the possibility of human error by minimizing human involvement. Given that it’s a self-learning and prediction system, the system would be able to create a trend or pattern of defects in the testing and apply that knowledge in the future as well. With the increased use of artificial intelligence, a huge load is taken off the testers so that they are able to focus more on strategic planning and other important tasks. This also allows the testers to avoid tedious and monotonous tasks that might lower their productivity levels. The ultimate customer processes can be improved with the use of machine learning technology which is based on the pattern recognition mechanisms. It helps the testers to analyze tons of information and deduce patterns and trends from it.