automation experts Request a DemoStart testRigor Free copy url copy url copy url copy url copy url copy url copy url copy url copy url copy url copy url copy url copy url copy url copy url copy url copy url copy url copy url copy url copy url copy url copy url copy url copy url copy url copy url copy url copy url copy url 15 Minutes Away Keith Powe VP Of Engineering - IDT © 2024 testRigor. All rights reserved. Cookie settings Strictly Necessary Cookies Always Enabled Non-Necessary Enabled We are seeing a revolution in our world today: Artificial Intelligence. It is so powerful that every aspect of our life is touched by its magic. Currently, AI agents are everyone’s field of interest based on their capabilities and the significant changes they offer. So, what exactly is an AI agent, and what does it do? Let us start with a simple definition: In AI, an intelligent agent is like a smart assistant that understands its surroundings and makes decisions autonomously on its own to reach its objectives. They can think and work independently after they receive a goal to achieve. These agents can be as simple as room temperature control or as complex as humanoids or Mars rovers. In this article, we will learn about the role of AI agents in software testing. We will discuss their features, types, and applications, specifically their use in software testing. AI agents aim to fulfill specific goals, depicted by an “objective function” that represents these goals. Once they have the goal, the agents will create the task list and start working on it using data learning, pattern recognition, making decisions, and moving towards achieving specific goals. For instance, reinforcement learning agents use a “reward function” to guide them toward desired actions. These intelligent agents are vital subjects in AI, economics, and cognitive science. They represent anything from individual programs to complex systems operating without any human intervention. Recent developments in generative AI allow AI to understand human languages. Therefore, AI agents are the connectors that weave AI into our real world and allow it to execute required actions autonomously. Below are the prominent features of an AI agent: AI agents can be broadly divided into five categories: Here are the common applications of AI agents in the real world: Here, we discuss the top applications of AI agents in software testing and how their use can uplift the whole scenario. It is helpful to have AI agents run the tests since they bring three main benefits into the software testing process: Let us learn about AI agents’ applications in software testing. Intelligent AI agents can automatically generate test cases based on the requirements and specifications of the software. They have the help of the below capabilities to do so: Natural Language Understanding (NLU): Regarding software QA, an AI Agent is a system that understands natural language as a description of what needs to be done and can execute that description as a test. The keyword here is “execute”. Let us consider, for an e-commerce website, you provide a prompt to the AI: “find and add a Kindle to the shopping cart”. It should execute a sequence of steps, allowing the user to find and then add a Kindle to the cart. Those actions might include entering a Kindle into the search, clicking on a specific product, and clicking the “Add to cart” button. It would be an executable test that validates the ability of the website to add a product to a shopping cart. Here is a video to learn more about how to use testRigor’s generative AI for software testing. testRigor is the #1 most advanced AI agent for software testing, which works on two levels: AI agents in software testing run the test cases automatically without needing any manual intervention. The test suites are triggered automatically when code changes are pushed into the repository. Also, the integrations with defect management systems allow defects to be raised and reports automatically shared with stakeholders. These agents can run the test cases 24/7 during parallel testing to achieve excellent test coverage without manual assistance. AI agents like testRigor exhibit self-healing capabilities, where test scripts automatically adapt to application UI or API changes without human intervention. This reduces the maintenance overhead of test scripts to a great extent and improves the automated tests over time. For instance, if a login button is changed from tag to tag, testRigor, as an AI agent, would care how the button is rendered and understand the button’s role within the application context. AI is vital in shift-left testing, where testing activities are performed earlier in the development lifecycle. AI agents can assist developers by providing real-time feedback on the potential impact of code changes, suggesting tests to run, and identifying possible defects during the coding phase. DevTestOps, TestOps, and test scalability, supported by AI agents, empower testing teams to achieve shift-left and deliver faster with excellent quality. You can use AI agents to analyze historical test data and identify areas of the software that are more prone to defects. These areas might require more intensive testing. This can help optimize the test suite, ensuring that testing efforts focus on the most critical and high-risk areas. During test optimization, redundant or irrelevant tests are also removed from the test suite to save testing time. AI techniques, especially those using machine learning and computer vision, can automate the process of identifying visual discrepancies in the UI of an application across different devices and screen sizes. You can automatically detect visual defects in web and mobile applications by comparing screenshots against a baseline. This helps in identifying visual regressions that might not be caught by traditional functional testing. Over time, AI agents can learn from the outcomes of past testing efforts to improve their accuracy and efficiency, reducing the manual effort required for testing. AI can analyze trends from past testing cycles to predict future test outcomes. This helps teams to anticipate and mitigate risks before they become critical. AI agents can monitor application performance under test to identify bottlenecks and optimize for better performance. By analyzing code and identifying patterns that may lead to vulnerabilities, AI agents can enhance the effectiveness of security testing. Here is a glimpse of our software testing future with AI Agents: “In the race for quality, there is no finish line.” – David T. Kearns AI agents like testRigor uplift software tests by automating repetitive tasks, providing more test coverage, and helping detect defects quickly using plain English. These agents can effortlessly optimize the overall testing process through generative AI and NLP. As AI technology evolves more with time, these agents are expected to become even more sophisticated and intelligent. This will further enhance their capabilities and applications in software testing. The trend has just started, setting a solid base for better quality, faster delivery, and minimum effort. The future looks promising and bright with these intelligent AI agents.