testRigor
The Emergence of Generative AI in Software Testing

Software testing was once manual, timeconsuming, and error-prone, hindering productivity. The rise of low-code and no-code platforms transformed testing by enabling automated tests without complex coding. Challenges persist, including inflexibility for unique scenarios and complex cases, complicating integration and customization. Large language models (LLMs) entered testing to streamline the process, yet these LLMs sometimes produced misleading outcomes due to biases and limited data.

Generative AI then emerges as a possible solution. It uses machine learning to process human input and generate test cases automatically, matching specifications and enhancing software quality. These generated test cases vary in format, reducing testing effort while enhancing accuracy. This AI-driven approach optimizes testing in the low code and no code environment, addressing its challenges while improving overall outcomes.

testRigor, an AI-powered test automation tool, streamlines the testing process by autonomously generating test cases based on the tester’s requirements. This eliminates the need to write test cases from scratch, saving both time and resources, and ultimately enhancing the quality of the final product. Using plain English, testRigor generates precise and less error-prone code in a significantly reduced time frame.

“Employing plain English for code generation enhances accessibility, readability, and overall productivity. This approach also leads to higher efficiency and substantially reduced maintenance costs, as developers can easily avoid or fix errors, communicate effectively, and adapt code to changing requirements,” says Artem Golubev, CEO of testRigor.

“Our system utilizes generative AI to deal with ultracomplex scenarios by constructing complete end-to-end tests using a series of regular steps and free-flowing English instead of only generating an example,” he adds.

Unlike many other no-code test automation tools, testRigor ensures that all tests are constructed from an end-user perspective. For instance, if a client is testing an e-commerce website’s shopping cart functionality, they can instruct the AI using plain language, such as “Add a Kindle to the shopping cart.” With a simple click, the AI generates tests that simulate real user actions on the website, step by step. This approach ensures the accurate replication of real-world tests on the live system.

testRigor’s innovative approach involves a different method than traditional generative AI, which relies on prompts to generate examples. Instead, testRigor’s system launches the website, logs into the system, and creates real tests on the actual target system. Errors in the system’s generated instructions can be easily corrected by clicking “update” and retesting after selecting desired preferences. This straightforward process ensures that the generated instructions execute as intended.

testRigor’s system is designed for user-friendliness and selfservice. All tests, whether manually written by testers or generated by the built-in AI engine, are presented in plain English commands. Customers can customize the tests themselves, dealing with deviations from facts or to adjust for contextual logic. This eliminates the need for specialized automation engineers with coding expertise.

testRigor serves a broad range of industries with its versatile software, covering native desktop, mobile, and web applications. It offers numerous advantages, like simplifying common tasks, which are applicable to all users. In addition, testRigor has collected data from various domains like finance, insurance, ed tech, medical records, and biotech, ensuring its effectiveness in diverse contexts. It seamlessly functions with pre-packaged applications like SAP, Infor, Salesforce, Microsoft Dynamics, ServiceNow, and many more.

Boasting an impressive client portfolio, including Netflix, Splunk, and Business Wire, testRigor continues to expand its reach. Clients consistently report substantial time savings and minimal maintenance efforts when using testRigor for test automation.

Looking ahead, testRigor plans to introduce new features, such as autonomous test case generation based on system descriptions and integration with test case management systems. The company’s objective is to optimize test automation using generative AI, minimizing manual intervention and maximizing test coverage. Efforts will focus on improving system functionality, reducing deviations, and increasing the overall success rate of automated tests.