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What Is Artificial Intelligence in Software Testing—Benefits and Challenges
A recent IBM study shows 77% of companies worldwide
already use or are exploring various AI technologies, including artificial intelligence in software. Software testing joins the race
to introduce effective AI tools. They have the potential to greatly enhance software testing efficiency, allowing developers to produce high-quality software faster and seamlessly. And the high quality of the code contributes to the brand’s reputation.
Savarian.tech, an innovative software testing company, was among the first to use AI in testing. The new tools helped us provide flawless services for our clients, reducing reputational risks and enhancing user experiences.
This article is the quintessence of our immense expertise in AI testing tools. After you read it, you’ll know how AI in testing can boost your business.
What is AI Software Testing?
Traditional methods include manual and automated testing. In short, while manual testing focuses on identifying faults from the user’s point of view, automated testing repeats manual operations to fulfill them without human intervention. To distinguish them better, read our article describing these approaches.
Unlike traditional software testing, AI-powered testing relies on artificial intelligence and machine learning technologies to assess data collected during the testing process and implement necessary changes.
If you want more details, contact us to learn how AI in software testing will boost your product development. We provide full-cycle software testing services, from the choice of AI tool to maintenance.
How AI Can Be Used to Improve Software Testing
The companies apply AI-powered software testing methodologies in
- Automatic test case generation
- Predictive analytics to anticipate issues
- Test data management, minimizing the time and effort of testers
- Enhanced defect detection, etc.
Thus, AI can be involved in each step of the testing process. Therefore, there are significant differences between manual, automated, and AI-based software testing. Let’s consider them closely.
How is AI-powered Software Testing Different from Traditional Software Testing?
To juxtapose traditional and AI-based testing, we compared their key characteristics. What are the advantages of AI in testing? This table illustrates them.
| Manual Testing | Automated Testing | AI-based Testing | |
|---|---|---|---|
| Speed | Low | High | High |
| Productivity | Low, as performed by humans | High | High |
| Accuracy | Not consistent, as even the most experienced testers make mistakes | Consistent | Consistent results with detailed feedback and additional possibilities of autocorrection. |
| Coverage | There are some omissions | Specific | Increased scope of tests and, therefore, overall test quality. |
| Scalability | A linear process is performed subsequently. The larger the scope, the more complicated preparing the test cases is. | Highly specific test cases. Human testers are required to write more cases. | AI produces the code for hundreds of test cases faster than a human. |
Compared to manual testing, automated testing has significant advantages. Read our article to learn more about it. Nevertheless, AI-powered software testing has an even more dramatic influence on software testing quality and efficiency. Let’s consider how AI testing helps QA professionals.
11 Benefits of AI in Software Testing
Consider each advantage of artificial intelligence in software testing from the list below.
Fast-paced Testing
Once established, automated tests speed the QA process up. The testing duration may be reduced from days to hours, resulting in cost savings. AI automated tests go beyond that. Artificial intelligence technologies gather data, interpret it, and implement necessary changes.
Better Defect Tracking
Traditional testing misses some bugs. In contrast, AI detects flaws more accurately. Moreover, AI predicts possible errors from the available data. Thus, the scalability of the project improves. As the number of test cases rises, the quality remains the same.
Additionally, AI testing tools provide code autocorrection and eliminate identical issues. For this purpose, AI studies failure fingerprints in debugging logs. With all these steps, defect tracking becomes more effective.
Script Automation
With automatic test scripts, there is no need to control or launch tests manually. Therefore, valuable human resources are saved.
Furthermore, AI-powered tools can learn themselves. For example, AI studies how pages load and obtains answers from the server. This data help to achieve more reliable results.
Evolving Bots
AI bots decide whether this or that change results in new functionality. AI bots continuously adapt as the code changes. As a consequence, the product’s quality increases because there is no need for constant manual corrections.
Enhanced Scope of Testing
AI testing tools examine test coverage. If they identify risk-prone locations with insufficient coverage, they will highlight them for additional attention. Moreover, artificial intelligence in software uses spidering to write multiple tests automatically. The gathered data help machine learning models to compare the predicted patterns for discrepancies.
Ultimately, manual tests cannot engage thousands of users in the study, but AI-based testing can. Moreover, unlike automated testing, AI can apply changes based on behavioral data to improve the code.
Visual User Interface Testing
The visual validation of interfaces is difficult to automate, as it requires human judgment. Nevertheless, machine learning tools are used to spot differences beyond the human eye’s capabilities. AI testing tools capture screenshots, calculate load times, develop a framework, and profile risks.
Better Test Maintenance
Software code can be significantly altered with time. As old test work cease to work, new tests are required. Writing new test cases substantially increases the time and cost of the process. Yet, computer vision bots minimize the expenditure.
Enhanced Writing of Test Cases
AI increases the quality of automated test cases. Moreover, AI generates different test case scenarios, analyzing the project data in seconds. The frequently used scenarios are the basis for new test cases created automatically.
Better Team Dynamics
Tests can be run automatically whenever the source code changes, notifying testers and developers of their outcome. The developers become more confident in their code while writing due to more consistent and objective testing of the AI testing tools. At the same time, testers receive the structured data.
Prognostic Analysis
AI testing tools use the current customer data to foresee future demand and user behavior. So the developers will prepare to make changes before customers realize they need them. A faster reaction to market volatility gives the product a competitive advantage.
Automated API Test Generation
API testing automation helps to create numerous test cases to estimate the operation of third-party technologies. AI analyzes vast amounts of data and quickly determines if the API works appropriately. As software solutions may employ hundreds of APIs, this possibility can come in handy.
3 Main Challenges of AI in Software Testing
Data Availability
A vast and structured data set is the main precondition to using AI in software testing. If the AI lacks data for analysis, the results may be unreliable. In this respect, AI testing resembles statistics.
Heavy Computational Load
AI implementation can be expensive, as it needs powerful hardware to operate. For example, some AI models from Facebook and Google are economically impractical for this reason. Fortunately, there are some affordable tools we’ll mention later.
If you plan to implement AI, prepare to invest in the most advanced Graphical Processing Units, analyze the operations with memory consumption estimation tools, and adopt a hybrid cloud infrastructure. These will help to lower investment resources.
Lack of Generality
AI-based tools are currently good at solving a specific task but lack generality, given their limited application scenarios. Therefore, the choice of the proper tool may take a long time.
When choosing an AI testing tool, consider several parameters. Identify the testing requirements as functional, performance, security, and usability testing have their peculiarities. Also, you need to keep in mind integration capabilities, ease of use, and vendor reputation.
To make the process of selecting an AI tool easier for you, we will describe the best examples of artificial intelligence software.
AI-Based Test Automation Tools
There is no universal tool, as each instrument solves specific problems. What software is used in artificial intelligence testing? Here are some noteworthy examples.
Launchable
Launchable uses pattern recognition to predict whether the test will fail. This data may be used to go through the testing suite and reduce apparent duplicates. With Launchable, you aggregate tests and perform only the most significant ones.
Percy
Percy is one of the AI testing tools for visual regression testing. It keeps your UI tests relevant and maintains user interface consistency across multiple browsers and devices.
mabl
mabl is a test automation tool with auto-healing capabilities. Its low-code interface simplifies test creation and maintenance.
Avo Automation
Avo is a specialized tool for handling tests with AI data-generating features. The solution claims to replicate real-world data sets.
Diffblue Cover
Diffblue Cover is an AI tool for creating unit tests. The tool works with Java code. It can be used as a plugin for IntelliJ IDEA and a command-line tool.
Diffblue Cover evaluates your code as you write it. Additionally, it generates unit tests that match your implementation and serve as a regression-testing suite.
FBInfer
Facebook Infer finds potential problems in source code before its release. It detects deeper infer-procedural issues that involve many files.
GitHub Copilot
GitHub Copilot is not among AI testing tools, it is an AI-powered coding assistant. Nevertheless, it can improve the code and reduce errors. Furthermore, nowadays, many engineers use Copilot as a unit test generator. This feature of GitHub Copilot will likely be enhanced in the future.
UI Path Test Suite
UiPath Test Suite combines the world’s greatest robotic process automation technology with best-in-class testing capabilities. It accelerates scaling through proactive testing and cross-enterprise collaboration.
Sofy.AI
It is an AI tool for testing Android and iOS-based mobile apps. It has a no-code testing platform that simplifies the process, shortens the testing life cycle, and decreases testing time.
Tricentis Tosca
It is an enterprise solution for end-to-end automated testing. This system includes many impressive features like Visual AI, model-based test automation, risk-based test optimization, etc. It combines deep neural networks with sophisticated heuristics to provide dependable, self-healing, platform-agnostic UI automation.
Functionize
Functionize is an automated solution for functional and non-functional testing hosted in the cloud. As many other AI testing tools, it speeds test generation and management. Additionally, this program can conduct hundreds of tests from all desktop and mobile browsers in a single minute.
Parasoft
Parasoft can be used at every stage of the software development cycle, from code analysis to user interface testing. Parasoft’s AI technology detects flaws early and ensures compliance with various functional safety, security, and code standards.
Eggplant AI
Eggplant AI explores software, identifies errors, and solves technical challenges. Additionally, it provides a graphical breakdown of the tests and findings.
Applitools
Applitools is a visual comparison tool relying on artificial intelligence in software for detecting and reporting changes in an application’s user interface. It takes minutes for this program to match hundreds of interface discrepancies at once.
Testigma
Testigma is appropriate for continuous automated testing. It writes automated quality tests using a natural language testing approach. Testigma finds and avoids unexpected failures by identifying the relevant test cases.
Testim.io
Testim.io writes, executes, and maintains automated tests for functional end testing and user interface testing. Testers may cover complex programming logic using JavaScript and HTML.
Appvance
Appvance generates test cases based on user behavior. The test portfolio encompasses everything that actual end users perform on production systems. As a result, it is entirely user-centric.
Test.ai
Test.ai is a mobile test automation platform that employs regression testing. It aids in obtaining performance data for your application and serves as a monitoring tool rather than a functional testing tool.
Functionize
Functionize has the same characteristics as other AI testing tools, such as designing tests and running multiple tests. However, it also provides powerful analytics to assist teams in test improvement.
Methods for AI-Based Software Test Automation
What is an advantage of AI? These proven methods of AI-powered software test automation will showcase the potential of the new tools.
Regression Suite Automation
Manual regression testing is time-consuming, while automated one requires significant initial investments in time and resources. AI automates this process and links it to code changes, reducing the testing period. Running regression tests becomes predictable and controlled.
Defect Analysis and Prediction
Machine learning and natural language processing reveal software problems. The primary purpose of this test is to ensure the early identification of errors. In its turn, proper defect prediction reduces time to market.
Self-Healing Automation
The AI-enabled self-healing approach includes automatic repair of test automation script failures caused by changes in the code. This test’s primary purpose is to ensure less manual intervention and ensure consistency of the process.
As you may see, artificial intelligence in software is not perfect. Nevertheless, it has a very promising future.
The Future of AI in Software Testing
The worldwide AI market reached $136.55 billion in 2022, more than the yearly net income of such tech giants as Microsoft and Meta. And it is expected to increase.
Specialists agree that AI algorithms will evolve and take over traditional testing in most cases. Nonetheless, human assistance will still be necessary to program the tasks, control the workflow and manage test results, setups, etc.
With the spread of AI testing tools, new solutions will emerge and adapt to changes in the software under test, automatically altering test cases.
Additionally, predictive analytics will be used more frequently in AI-powered testing systems to foresee future faults and offer corrections. This will allow testers to fix issues before they become significant.
Furthermore, AI-powered testing will be more tightly linked with DevOps, allowing quicker and more efficient testing in continuous integration. As a consequence, teams will be able to detect and fix issues sooner in the software development lifecycle, producing higher-quality software.
Overall, the success of AI in software testing will boost AI implementation in other stages of software development.
Wrapping up
AI is the future of software testing since it delivers more reliable and scalable results. With the help of AI testing tools, companies automate testing procedures, decrease expenses, and enhance the overall quality of their software products.
Still, nowadays, there are several challenges on the way to the common usage of AI tools. They include the lack of training data, general complexity, and high expense. Nevertheless, we believe these challenges may be reduced by proper design and execution.
If you want to know more about how AI-powered software testing will benefit your products and your company, get in touch. We will gladly share our experience with you and ensure the top-tier quality of your products. Our experts will implement the most suitable AI testing tools depending on the specifics of your project.
