Software developers are constantly seeking to improve accuracy and reduce costs in their testing cycles. In the last couple of years, CIOs turned to Artificial Intelligence (AI) and Machine Learning (ML) in an attempt to reduce the development cycle and release high-quality products. Discover below how AI and ML will redefine software testing and improve software quality and performance.
Characteristics of Machine Learning Useful in Software Testing
Machine learning is a subset of AI and enables machines to self-learn without being explicitly programmed. When exposed to new data, machine learning-enabled programs are able to learn, grow, and observe patterns without the need for programming.
Machine learning already proved itself useful in data analysis and quality control by predicting and optimizing key processes. Both Black Box testing and White Box testing can be done using ML.
Using AI to safeguard quality assurance will give you information on common errors, critical effects, and malfunction patterns that can risk the stability of your software. When assessing machine learning capabilities for software vendors, keep in mind that test automation can execute 1000+ different test cases in every test run providing coverage that is not possible with manual tests.
For example, the startup known as Launchable promises to make software testing smarter and faster by including machine learning in the process. The company’s technology predicts the likelihood of failure for each test given a change in the source code. Its goal is to eliminate slow feedback from tests, allowing users to run only the meaningful subset of tests in an order that minimizes feedback delay.
Key Benefits of AI and ML in Software Testing
Going Beyond the Limitations of Manual Testing
Automated testing can simulate thousands of virtual sets of users that interact with networks, web-based applications, and software. ML provides more test coverage to the test cases whenever an application is modified or updated. Moreover, testers can also use AI bots that adapt and learn application functionalities.
Even the most meticulous testing team is bound to make mistakes while carrying out monotonous tasks. This is where automated testing really helps. By performing the same steps accurately every time, the system never misses out on recording detailed results. Freed from repetitive manual tests, employees have more time to deal with sophisticated features.
Saving Precious Time and Money
Manually repeating tests not only are time-consuming, but extremely costly. In contrast, automated tests can be executed again and again at significantly lower costs. In some cases, the testing time could be reduced from days to hours, which translates directly into cost savings.
Understanding and forecasting client requirements is another benefit of ML often ignored by software vendors. Analyzing customer data (client requirements, behavior, user experience) to full potential enables accurate predictions on the company’s products and features as they need them.
ML systems are now being implemented in the quality assurance industry and testing, as IT organizations accumulate vast amounts of data on defects.
Improving the Overall Quality
Traditional software development means thousands of lines of code written one after another. Manually testing each line of code takes weeks—in some cases months—and has a higher chance of error. ML accelerates the entire process, easily managing files to enhance program accuracy and reliability, and improve the overall quality.
Moreover, developers use shared automated tests to catch problems quickly and notify Quality Assurance engineers. Features like these save precious time and increase developers’ confidence.
Stop Struggling with Bugs
One of the biggest problems in software testing is overlooked bugs, which tend to have adverse consequences on a company’s reputation and bottom line. ML often implies a reliable testing approach by learning from previous bug patterns in code and applying it to a similar code pattern.
Despite their hard work, QA engineers can easily fail to detect bugs. AI evaluates test cases and various error incidents in a short span of time. With the help of AI-enabled bots, testers can quickly decide the course of action or track bugs.
Machine learning has already started to transform the way companies do business. As speedier delivery becomes mandatory, organizations planning to stay at the top of their game must embrace ML and AI as part of their software testing practices.
