The future of automation and artificial intelligence has become a popular topic of discussion. While it’s true that some concerns have been raised about job displacement, let’s focus on a more positive outlook for this blog post. AI has emerged as a significant technological advancement, with new tools being introduced every day. With this in mind, I wanted to explore how AI can revolutionize software testing and quality assurance.
To get started, I decided to try out various tools and examine their features. Surprisingly, I discovered an abundance of choices, all claiming similar functionalities. As a dedicated QA professional, it falls upon me to put these tools to the test. Consequently, I’ve compiled a list of eight tools that offer free trials and I provided a summary of their claimed capabilities. It’s important to note that the information I’ve gathered is solely from their respective websites, and I haven’t received any compensation for featuring them. Now, let’s delve into how AI can benefit software testers.
Advantages and Benefits of AI in Software Testing
Before introducing the tools, it’s crucial to offer a concise explanation of the advantages brought by AI testing tools. AI can greatly enhance software testing, offering a wide range of benefits to developers and testers alike. One prominent advantage is the notable improvement in accuracy. Even the most proficient testers can make errors when performing repetitive manual testing tasks. By integrating AI into the testing process, automated testing ensures the precise execution of identical steps consistently, eliminating the possibility of overlooking crucial details and guaranteeing accurate outcomes.
Moreover, AI-powered automation testing goes beyond the limitations of manual testing. It becomes nearly impossible for large software or QA departments to conduct controlled tests with thousands of users. However, with automation testing, it becomes feasible to simulate tens, hundreds, or even thousands of virtual user systems that can interact with networks, software, or web-based applications, enabling comprehensive testing scenarios.
AI also benefits both developers and testers by enabling the use of shared automated tests. Developers can utilize these tests to identify and address problems early on, before passing the code to the QA team. With automated tests running automatically whenever the source code changes, developers gain confidence and save time in the development process. In addition, automated AI software testing leads to an increase in overall test coverage. It allows for deeper and broader testing by examining memory and file contents, internal program states, and data tables to ensure that the software functions as intended. This expanded scope enhances software quality and reduces the risk of undetected issues.
Finally, AI-based automation testing significantly reduces the time to market. Manual testing requires repeating tests every time the source code changes, which can be time-consuming and expensive. In contrast, automated tests can be executed repeatedly, at no additional cost, and at a much faster pace. This not only saves time but also translates into cost savings, as the software testing period can be reduced from days to mere hours. Overall, AI empowers software testing with enhanced accuracy, scalability, efficiency, and cost-effectiveness.
Key Features and Promised Advancements
From what I’ve observed, no particular tool stands out in terms of its features. They all seem to offer more or less the same promises. I attempted to outline the key points.
- Automated unit test creation: AI-based products can be used for automated unit test creation, especially for organizations that plan to introduce continuous testing, continuous integration, or continuous delivery in DevOps
- Test maintenance: AI-based tools can evaluate changes to the code and fix several existing tests that don’t align with those changes, especially if those code changes are not too complex. Updates to UI elements, field names, and the like need not break the test suite anymore
- Test generation: AI tools can swiftly and automatically develop test scripts. AI tools may be taught based on past project inputs and results to automatically generate test scripts for comparable projects
- Self-healing: Some AI tools monitor running tests and try out modified variants for failed tests by choosing UI elements based on the best fit. They can also verify test coverage and supplement the gaps if needed
- Cross-browser testing: AI-based test automation platforms can run tests on any browser in the cloud and self-heal as your site changes
- Test automation productivity and stability: AI-based tools leverage AI and ML and have significant abilities related to Robotic Process Automation (RPA), Natural Language Processing (NLP), Model-based Test Automation (MBTA), and these tools aim to enhance test automation productivity and stability. It. enables faster and continuous testing, complete automation without any human intervention, and better test coverage
- Better low-code solutions: With the aid of AI tools, low-code solutions can be further improved, enabling even testers with limited experience to create automated tests and scripts. This advancement allows for greater accessibility and empowers individuals with varying levels of expertise to contribute to the testing process effectively.
AI Testing Tools
Katalon platform
The Katalon platform is a comprehensive automation testing software tool developed by Katalon, Inc. It is built on top of the widely used open-source automation frameworks Selenium and Appium. The platform offers a specialized IDE interface designed for testing web, API, mobile, and desktop applications. It provides teams with a comprehensive quality management platform that supports testing of various applications and APIs. The platform offers a dual interchangeable interface for creating test cases, catering to both non-technical users with a manual view and experienced testers with a script view featuring syntax highlight and intelligent code completion. It also facilitates the execution of automated tests on UI elements, such as pop-ups, iFrames, and wait-time. The Katalon Platform is compatible with Microsoft Windows, macOS, and Linux operating systems.
Here is the pricing for Katalon:
TestRigor:
TestRigor is a software testing tool that leverages AI to simplify and accelerate the testing process. It allows manual testers to automate tests 15 times faster than traditional methods and reduces maintenance time by 99.5%. The tool enables the creation of stable tests in plain English, emulating the way humans interact with web, mobile, and desktop applications.
It automatically generates tests based on the AI’s mirroring of how end users interact with the application in the production environment. This AI-driven feature should significantly reduces the time spent on maintaining test scripts and increases the reliability of automated tests.
Here is the pricing for TestRigor:
Headspin:
HeadSpin is a comprehensive software testing tool that offers a range of capabilities for mobile application testing. One of the major premises of HeadSpin is its ability to provide real-time insights into user experiences across different devices, networks, and locations. It allows testers to simulate various network conditions, such as low bandwidth or high latency, to assess the app’s performance in different scenarios. The tool also provides detailed performance analytics, crash reports, and user session recordings, enabling developers to identify and resolve issues effectively. Furthermore, HeadSpin supports both Android and iOS platforms.
Pricing: Not listed
Testim
Testim is a modern software testing tool that leverages artificial intelligence to provide fast authoring of stable tests and tools that help scale quality. Like other compatitors, it uses AI-powered Smart Locators that understand your app, lock-in elements, and self-improve to deliver stability. This should result in a reduction of maintenance time and resources spent on flaky tests. It allows for the quick recording and configuration of web or mobile tests with flexibility through customized JavaScript steps. It supports Web and mobile applications.
Mabl
Mabl is a low-code test automation solution that enables software teams to tightly integrate automated end-to-end tests into the entire development lifecycle. It offers a platform that simplifies the creation, execution, and maintenance of reliable browser, API, and mobile web tests, thereby accelerating the delivery of high-quality, business-critical applications. Mabl’s features include auto-healing tests, cross-browser testing, mobile testing, and API testing. It also offers performance testing as an add-on feature.
Here is the pricing for Mabl:
Functionize
Functionize is a software testing tool that uses artificial intelligence to provide enterprise-grade test automation in the cloud. It uses machine learning, big data, and computer vision to create self-healing end-to-end tests that can run at scale. Functionize’s AI algorithms should understand your application and adapt to changes, reducing the maintenance time and ensuring test stability. It offers features such as Architect Smart Recorder for test creation, Extensions for reusable custom code, cloud execution, and integrated visual testing.
Pricing: Not listed
TestCraft (Perfecto)
TestCraft is a codeless Selenium test automation platform that uses AI-powered technology to create and execute tests quickly and easily. It uses AI to eliminate the challenges of maintaining and changing test scripts, making it a highly efficient tool for continuous integration and delivery. TestCraft’s AI algorithms can identify dynamic elements and adapt to changes in the application’s structure, reducing the time spent on test maintenance.
Here is the pricing for TestCraft:
aqua ALM
Unlike the other tools, aqua is not a pure testing tool but focuses more on test management in general. Aqua claims to be highly scalable and can handle huge projects even if it means moving millions of items from other systems to their infrastructure quickly. Aqua ALM provides the history of approvals, test cases, their effects, and results in a single centralized place. These can be used to track projects and progress quickly. The testers can choose to opt for cloud or on-premise infrastructure with a promised 1 day migration time. Aqua ALM software testing tool comes with a Chrome extension that they claim to speed up manual testing by 40%. Manual and automated tests can be directly written in aqua too.
While the descriptions of these tools on their respective websites may sound promising, it’s important to consider that marketing efforts can sometimes overstate their capabilities. To gain a better understanding, I’ve decided to personally test some of these tools and provide detailed feedback based on my experience in the future. It’s worth noting that the effectiveness of such tools ultimately depends on the user’s proficiency in utilizing its features. Furthermore, considering the diverse range of projects with varying requirements, it will be intriguing to see how these tools perform in different environments. If you have any experience using these tools or similar ones, I encourage you to share your insights and experiences.
I hold an optimistic outlook on the advancements in software testing facilitated by AI. The continuous stream of innovations has the potential to reshape the testing landscape and simplify various aspects of the process. It appears that the role of a software QA engineer is transitioning more towards defining testing processes and determining what needs to be tested (Quality Assurance), rather than solely focusing on test execution (Quality Control). It is an exciting time as we witness the transformative impact of AI in software testing, and I am genuinely curious to see how it will continue to shape the industry and revolutionize our approach to software quality assurance.
