AI in Software Testing
What is AI software testing?
The digital world has seen an exponential increase in the frequency of software releases over the past few years, escalating the need for quicker, more efficient testing processes. The pressure to deliver flawless software is at an all-time high, and there’s universal agreement that test automation, even when partially implemented, can significantly alleviate this strain.
Nevertheless, a common issue most companies face is that their test coverage is not as comprehensive as they’d prefer. Why is this so? The answer typically lies in the inefficiencies embedded within most of the prevailing test automation frameworks. These can manifest as:
- A complex initial setup process that takes considerable time and resources
- Intricate test implementation procedures that require extensive coding skills and deep technical knowledge
- Tedious test maintenance, which is time-consuming and labor-intensive, leading to inefficiencies and delays
But what if you actually can?
Artificial Intelligence (AI) software testing is a game-changer, showcasing the potential to revolutionize traditional approaches to software testing. It offers a range of benefits, including:
- Automatically generating test cases, effectively reducing the time required for test creation
- Providing assistance during test creation, making the process simpler and more intuitive
- Improving test stability, thus reducing the likelihood of erroneous results
- Detecting elements on the screen, thereby aiding in more accurate test execution
- Automatically identifying issues, helping in proactive problem resolution and enhancing the software’s overall quality
Our customers have been leveraging these advanced features to dramatically boost their QA testing efficiency. They’ve already run over 100 million tests on the testrigor.com platform! This translates to millions of QA hours that were saved from being consumed by mundane, repetitive tasks. These valuable hours were instead reallocated to more strategic testing activities, leading to improved quality outcomes.
Does this signify that QA roles are on the brink of becoming obsolete due to automation? We strongly believe this is not the case. There is no concrete evidence pointing towards the elimination of QA roles in the foreseeable future. However, what we foresee is a metamorphosis in how QA teams function.
Historically, there has been some level of disconnection between manual and automation QA personnel within a team. However, this disconnect diminishes when everyone on the team is equipped to handle both manual and automation aspects of QA. This unified approach ensures that all team members can fully participate in the quality assurance process, from inception to completion.
We envision QA teams becoming more robust and thriving in this AI-powered landscape, enhancing their capacity to deliver superior quality software faster and more efficiently. This is not just about adopting new tools; it’s about fostering a new mindset and transforming the very essence of QA practice.
AI in Software Test Automation
How can AI be used in QA?
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.
Artificial Intelligence in Software Testing
Benefits of Using AI for Testing
AI-enhanced test automation tools such as testRigor offer advantages over traditional automated tools in pretty much every aspect. Let’s see how these differences can directly benefit your team.
With traditional test automation frameworks, initial setup typically takes anywhere from a few days to a couple of weeks. Setup is only the tip of the iceberg, though. Writing tests is complex and very time-consuming (in case each test is properly written from scratch, not copy-pasted).
Compare it to an AI testing tool such as testRigor: where initial setup takes just 5 minutes, and creating an automated test takes about the same amount of time as writing steps for a manual one. Forget about CSS Selectors and XPaths, refer to the elements just as you see them on the screen, and the ML algorithms will do the rest for you.
This feature is unique to AI testing tools. In a nutshell, it allows the software testing tool to analyze your website and automatically create test cases based on the most frequently used scenarios. It is ideal for cases where you don’t have any UI automation test coverage in place yet.
UI end-to-end tests are never 100% reliable with traditional testing tools since they have many dependencies (including third-party) by the nature of these tests. This leads to such tests breaking rather often, and maintenance of existing tests becomes a laborious daily task.
testRigor is solving these issues on multiple levels, bringing the automation experience to a whole new level:
- No need to think about implicit/explicit wait times, waiting for an element to appear on screen, etc. – testRigor takes care of everything.
- Even if the underlying server or a browser crashes while your tests are running – the system won’t raise an error but will automatically re-run the test.
- Usually, if the company decides to move to a different development framework, the QA team must go through every single test and spend countless hours updating the selectors. Not with testRigor! For example, as long as your “Add to cart” button stays in the same place, the test will continue to pass.
- Even when tests legitimately fail because of a software change, they will be automatically grouped by each element – making fixing them a breeze. That is how testRigor’s users typically spend 95% less time on maintenance compared to other automation tools.
There are a lot of factors, some of which we’ve already discussed, that contribute to a much fuller test automation coverage with an AI software testing tool:
- test creation and test maintenance take significantly less time
- autonomous test creation
- anyone on the team, including manual testers, can easily build automated tests (since no coding skills are required)
Want to know how these benefits translate into numbers? Here’s how a large telecommunications company was able to increase its test coverage from 34 to 91% in only 9 months.
Fuller test coverage results in the ability to run tests more often, and identify bugs much sooner than if tested manually or with other tools. And the QA team can allocate more time towards testing new features, defining edge cases, and building more automated tests – all of that instead of manually going over and over through the same set of test cases. Sounds like a win-win for everyone, doesn’t it?
Traditional test automation requires experienced test automation engineers for setup and test creation, and there’s no way around it. AI-enhanced codeless test automation enables everyone on the team to create the same automated tests while also making the process much more straightforward and efficient.
Cost savings don’t end here, however. As we’ve already discussed, we can now build automated tests faster, spend substantially less time on test maintenance, and have fuller test coverage. All of these benefits lead to lesser bugs making it to production, saving companies both money and reputation.
Since there’s no more code with testRigor, and all tests are written in plain English language – everyone on the team can contribute to test creation and make sense of already created tests. That promotes excellent visibility, team dynamics, and removes obstacles. An added benefit here is that it is now much easier to revisit previously created tests, modify or add more assertions, or perhaps discontinue them – if the functionality has changed.
AI Software Testing
For all the skeptical people out there: yes, we know what you’re thinking. General AI in its true meaning is not available yet, and isn’t expected to become available in the next 20 years or so. The reality is that AI and ML terms are being used interchangeably all across the internet. We’ll spare you the time and won’t contemplate how and why it happened, as it’s not the goal of this article. What’s important to know, though, is that testRigor uses multiple Machine Learning models to help our customers improve the efficiency of their QA testing as much as it’s technically imaginable with the latest infrastructure available. And even more importantly, these possibilities come at a reasonable price.
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:
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Recognition of Text, Images, and Image InscriptionstestRigor 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.
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Website Analysis for Autonomous Test CreationOne 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.
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Identification of Sudden Pop-ups and BannersAI 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.
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Classification of Image TypestestRigor’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.
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Conversion of Code into Executable Test CasesOne 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.
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Detection of Broken PagesThe 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.
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Building Tests Based on User Interaction AnalysisOne 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.
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");
}
}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.
AI Testing Software
AI Testing Tools
Functionality & Features: traditional tools typically have many limitations regarding certain functionality. Not all AI tools will be capable of covering all/most of the test scenarios you have.
Usability: the tool should be easy to adopt and easy to use. If it’s cloud-hosted, then most of the headaches will likely be taken care of for you.
Value for money: when evaluating AI automation testing tools, many people pay the most attention to the tool’s price. However, the only meaningful metric is the end result. You ought to factor in the ease of initial setup as well as the speed of test creation. And don’t forget about the amount of time spent on maintenance! Only make your decision once you understand the above pieces well.
Debugging: how easy is it to figure out what exactly happened when the test failed? Are there detailed screenshots for each test step, perhaps even an entire video?
Integrations: does the testing tool have integrations with your other tools? Perhaps you use Jira for issue tracking, store your test cases in TestRail, and have a CI pipeline? Only a few AI-based test automation tools will be able to easily support your workflow.
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Kieth P.
VP Of Engineering – IDT
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AI in QA Automation
We hope this article has provided illuminating insights into how the software testing industry is undergoing a profound shift with the integration of artificial intelligence and machine learning algorithms. The advantages and efficiencies these technologies bring to the table have begun to revolutionize the way we approach software testing.
Switching to a next-generation test framework like testRigor, which seamlessly incorporates the newest technologies, might be a compelling option, even for teams with fully established automated testing processes. Its unique features and capabilities present a significant leap forward from traditional testing methodologies.
Looking towards the future, we see AI playing an increasingly crucial role in QA automation. The technology is set to continue advancing, bringing in even more sophisticated capabilities and further optimizing the testing process.
For instance, predictive analytics, a subset of AI, could be used to predict potential issues and bugs before they even arise, effectively introducing a new dimension of preemptive testing. This approach would mitigate the risk of software failure and improve overall product quality.
Additionally, we can foresee AI-based tools becoming increasingly proficient at learning from historical data to optimize testing processes. By analyzing past tests, AI could identify patterns and trends, using this information to enhance future testing strategies and focus on areas most likely to harbor defects.
Therefore, embracing AI in QA automation now is not just about reaping the immediate benefits. It’s about preparing for a future where AI becomes an integral part of software development and testing, driving efficiency, quality, and innovation.
In conclusion, we encourage you to explore these transformative possibilities by creating a free account and experiencing the tool firsthand. Our pricing model is highly flexible, adapting to your specific needs – whether it’s the platforms you wish to support, the desired speed of receiving test results, the number of mobile devices, and so on. As we move forward, we eagerly anticipate how AI will continue to shape and enhance the landscape of QA automation, simplifying the test creation process to the fullest extent technically possible.
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