AI in Software Testing

AI in Software Test Automation

Delving deeper into the intricacies of traditional test automation reveals certain inherent challenges that often pose significant hurdles in its efficient implementation. Here, we will explore these challenges and demonstrate how the advent of AI in software testing has the potential to mitigate them.

Firstly, traditional test automation demands highly skilled engineers who excel not only in the technical aspects of setting up the test framework but also in crafting automated tests. Conventional automation frameworks are often rigid when it comes to their structure and architecture. Essentially, while there are myriad ways to construct tests that yield pass or fail results, only a few of these methods guarantee reliable tests that accurately validate the right aspects of a software application.

The second challenge arises from the design perspective of the traditional automation framework. Traditional automated tests necessitate a degree of knowledge about the underlying implementation details of the software under test. As a result, tests are usually constructed from an engineer's perspective rather than from an end-user's viewpoint. This means that elements are often identified by their technical identifiers, such as IDs or XPaths, rather than their contextual usage or appearance, as perceived by a user.

Thirdly, traditional automation tests suffer from complexity and relatively low readability. Consequently, once automated tests are set in place, they are seldom revisited to reassess their relevance, accuracy, or effectiveness. This could lead to outdated or inefficient tests persisting in the test suite, reducing the overall efficiency of the testing process.

The integration of AI into software testing has opened up new avenues to address these longstanding issues effectively. By employing AI, it is now possible to significantly simplify the test creation process, bridge the gap between the perspectives of end-users and engineers, and promote the regular reassessment of automated tests to ensure their continued relevance and effectiveness.

In the following section, we will dissect each of these areas in more detail, explaining how AI is transforming the landscape of software testing, enhancing its accuracy, efficiency, and overall impact. We will delve into the mechanisms of AI-based testing tools, demonstrating how they overcome the challenges of traditional frameworks and paving the way for a more robust, user-centric approach to software test automation.

testRigor is a unique software testing platform that integrates AI technology comprehensively in its testing processes, setting it apart from conventional testing tools. Here are several key ways this application of AI transforms the test creation process:

• Recognition of Text, Images, and Image Inscriptions
  testRigor employs AI to discern various types of content present in the application being tested, such as text, images, and inscriptions within images. This capability is crucial for accurately testing interfaces that heavily rely on visual elements and text-based interactions. AI can not only recognize these elements but also understand the context of their application, enabling more comprehensive and nuanced testing.
• Website Analysis for Autonomous Test Creation
  One of the standout features of testRigor is its ability to analyze a website or application independently and generate relevant test cases. The software scans the site and learns from its structure, functions, and user interaction patterns. It then uses this information to automatically create meaningful and useful test cases that address common user scenarios and potential edge cases. This reduces the burden of manual test case creation and ensures comprehensive coverage.
• Identification of Sudden Pop-ups and Banners
  AI can also be instrumental in dealing with unexpected elements such as sudden pop-ups and banners that can interfere with test execution and cause false negatives. By identifying these elements in real-time, testRigor can adjust its test execution strategy on the fly to account for these potential disruptions, ensuring that the test results are accurate and reflective of the software's functionality.
• Classification of Image Types
  testRigor's AI can classify different types of images such as arrows, download buttons, dropdowns, etc., in the application. This ability is significant as it allows the software to understand the user interface on a deeper level and to interact with it more effectively during testing. This image recognition feature can also contribute to better accessibility testing, ensuring that the application is user-friendly and inclusive.
• Conversion of Code into Executable Test Cases
  One of the key features of testRigor is its ability to convert code into human-readable and executable test cases using its record-and-playback tool. This feature can greatly simplify the test creation process and make it more accessible to non-technical team members. It can also lead to more robust test cases, as the AI can generate tests that consider multiple execution paths and edge cases that a human might overlook.
• Detection of Broken Pages
  The AI used by testRigor is capable of detecting whether a page appears broken to an end-user, for instance, when CSS is missing or not loading correctly. This helps catch issues that might negatively impact user experience but could easily be missed by traditional automated testing tools.
• Building Tests Based on User Interaction Analysis
  One of the most innovative ways testRigor uses AI is in building tests based on the analysis of real user interactions with the application in a live production environment. This enables the creation of tests that reflect actual user behavior, ensuring that the software is tested in a way that aligns with real-world usage patterns. This can lead to the discovery of issues that might have been missed by more abstract or theoretical test cases.

This sounds great on paper, but what does it actually mean for our customers? The differences are so massive that it's easier to show than to explain.

Here's what the most basic automated test looks like:

package tests;

import org.openqa.selenium.By;
import org.openqa.selenium.WebDriver;
import org.openqa.selenium.WebElement;
import org.openqa.selenium.chrome.ChromeDriver;
import org.testng.Assert;
import org.testng.annotations.Test;

public class TestSample {
  @Test
  public void login() {
    System.setProperty("webdriver.chrome.driver", "path of driver");
    WebDriver driver = new ChromeDriver();
    driver.manage().window().maximize();
    driver.get("https://www.browserstack.com/user/sign_in");
    WebElement username = driver.findElement(By.id("user_email_Login"));
    WebElement password = driver.findElement(By.id("user_password"));
    WebElement login = driver.findElement(By.name("commit"));
    username.sendKeys( ...charSequences: "[email protected]");
    username.sendKeys( ...charSequences: "your_password");
    login.click();
    
    WebElement welcomeXpath = driver.findElement(
      By.xpath("//*[@id=\"wpbody-content\"]/div[2]/header[2]/div/nav/ul/li[5]/a/span");
    )
    String welcomeCXpathText = welcomeXpath.getText();
    Assert.assertEquals(welcomeXpathText, s1: "Welcome Peter");
  }
}

And here's the same test with testRigor:

login
check if page contains "Welcome Peter"

The second example looks so clean; it's almost unbelievable at first glance. Also, you might've asked yourself - where did the login credentials go, and why are they not part of the test? The answer to this is simple, and it's just one of many perks of using testRigor. When creating a new test suite, you have an option to specify the login and password for your account. Afterward, "login" command in any test within your test suite will do just that, and if you were already logged in - simply skip it (instead of failing the test).

Quite neat, right? This is not even 1% of what you can do with testRigor. We will cover more features in the next section.