Exploring AI in Test Automation
Tags: test, automation
Published: 2023-09-20 16:09:03

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/10punarhuio8jv9tzeub.png)
Let’s jump into how new developments in Artificial Intelligence are transforming test automation. Organizations across the globe are using AI to tackle software development challenges like tight delivery timelines and complex ecosystems. We’ll highlight how AI can make the software testing process smarter, more reliable, and efficient. To begin, let’s examine some of the challenges and pain points in the testing process that call for AI-assisted automation.

**Challenges With Traditional Test Automation**

1. **Object recognition**
Object recognition is the cornerstone of traditional test automation. Traditional test automation tools take help of attributes like names, IDs, classes, XPath, or CSS selectors to identify the objects. Once the object is identified, the automation script can interact with it, simulating user actions like clicking buttons, entering text into fields, selecting options from dropdowns, and verifying the displayed content.

When these attributes are changed, the traditional test automation tools fail to recognize the objects. This leads to flaky test scripts. Manual identification of these objects takes a long time, and it can be very cumbersome to update the object repository. Thus, the overall testing process becomes slow and cumbersome. 

2. **Determining the size of regression test suite**
What to test and what not to test is still often a million dollar question for companies. QA professionals pick up regression test cases based on their experience. A few guesses are made to determine regression test scenarios which often leads to over-testing that consumes too much time or under-testing that exposes your business to unnecessary risks. 

3. **Test data management**
Test data is critical especially when you’re testing enterprise applications like Oracle, SAP, Dynamics 365 or Workday. Testers need data to simulate real-world scenarios and interactions that users will encounter. Without appropriate and relevant test data, the testing process could miss critical issues, and the overall quality of the application might be compromised. 

4. **Determining realistic workloads for performance testing**
As load testing requires understanding of peak traffic times and usage scenarios, it is difficult to design a workload that accurately reflects the actual usage of an application. Another problem is synchronizing multiple virtual users to mimic concurrent user behavior accurately.

**Watch now**: Mastering Oracle EBS Load Testing With Opkey
As you’re now aware of the shortcomings of traditional test automation tools, let’s dive deep to understand how AI based test automation tools can address these challenges. But before that:

**Introduction to AI**

AI, or Artificial Intelligence, refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses a wide range of techniques, technologies, and algorithms that enable computers to perform tasks that typically require human intelligence. These tasks include understanding natural language, recognizing patterns, making decisions, solving problems, and learning from experience.

**AI is further classified as**

**Generative AI**: Generative AI involves training a model to generate new content that is similar to a given input by leveraging natural language processing (NLP). It generates data that is coherent, meaningful, and often realistic within a particular domain. NLP helps computers understand, interpret, and respond to human language in a way that makes sense to us.

**Machine Learning**: ML refers to the way that computers learn to do tasks by getting better at them as they gain more experience, without being explicitly programmed to do so. Alternatively, traditional automation requires preset programming. 
Learn more: How Is AI Shaping ERP Implementation Process

**How can AI address these challenges**?

**Visually locating an element**

In dynamic applications, using locators might result in unstable test scripts, causing significant challenges in maintaining regression tests. The use of AI in software test automation can perfectly address this challenge. Instead of relying on DOM properties, AI can identify elements through visual cues, much like a human user (e.g., icons such as My Account, play, pause, mute, cart, and settings). By specifying the element itself, rather than offering locators based on HTML DOM properties, AI test automation tools can execute user actions seamlessly.

**Self-healing of test scripts**

To deliver a seamless user experience, dev teams keep on adding new features. Due to this, QA teams need to perform regression testing on a weekly/ monthly/ yearly basis. However, test script maintenance is a biggest challenge as it eats up all the benefits of test automation. AI driven test automation tools can easily identify changes in the UI and rather than failing the test script, they update the test scripts without requiring human intervention. 

**Auto generation of test scripts**

AI-powered test automation platforms can make test script authoring a breeze as non-technical users can directly feed test cases in English language. Leveraging NLP, AI tools can generate automation scripts by reading the test steps. Generative AI can interpret the intent of the user and has the ability to mimic those actions on the software applications. Thus, testers don't need to write any code for automating the workflows. It reduces the test script design time and effort by over 70%.

**Smart regressions**

Software testing with AI solves the problem of risk coverage. AI based tools consider more parameters while identifying the size of the regression test suite than are humanly possible. With this, rather than running the entire regression suite, QA teams can run only the impacted test cases. This will save time to a great extent. 

**Test data management** 

Due to compliance requirements, production data cannot be used as it is for testing purposes. AI can solve this problem and can help in masking data. Furthermore, AI can better slice and dice test data by understanding trends and patterns, helping QA teams in testing. 

**Workload simulation and synchronization**

AI can analyze historical user behavior data and generate realistic workload scenarios. It can help to simulate traffic patterns that closely resemble actual usage, and create automated tests that fill in any g. AI can synchronize virtual users' actions more accurately to get more accurate performance test results. 

**Conclusion**

Undoubtedly, test automation with AI is the way of the future. Emerging technologies like AI and ML significantly reduce the time and efforts required in creating and prioritizing test cases. Opkey is an AI-powered test automation platform that makes testing effortless. Its AI based change impact assessment automatically identifies the impacted test cases and recommends test cases based on the highlighted risk. This prevents QA teams from running redundant tests and running only those test cases that are relevant, saving time and improving coverage. Opkey automatically identifies broken test scripts and heals them autonomously without requiring human effort, making test script maintenance effortless. Want to know more about Opkey?