What Is Codeless Test Automation and Which Tools You Need for It

Codeless automation is becoming increasingly popular. Experts call it the next wave of Low-Code and even the future of testing. Many companies are trying to use these tools in their practice to save resources. Thus, they seek to empower QA teams and solve many of the problems typical of traditional automation. In this article, we will describe the features of the new approach and make a list of popular tools in this direction.

Key Features of Codeless Automation

Let’s start by looking at the difference between low-code, codeless, and no code test automation. The second and third are similar in meaning. They represent an approach in which the automated tester (AT) does not write code. Many codeless tools work on the principle of record and playback: first, recording the script (by the user/bot), and then making changes to the test in the editor.

As for low-code, we can say that this approach consists of two elements: codeless and flexibility. The main difference is that low-code makes it possible to add code to a test. Logically, you need to have coding skills to complete this task. This is one of the main disadvantages of such testing. Thanks to codeless testing tools, anyone can do automation.

Comparison of low-code and codeless approach:

As you can see, the approach provides freedom of action for a user far from the world of coding. It provides speed when testing products. It makes no sense to compare with traditional testing since the answer is obvious: automation without coding is easier and faster. However, it should be understood that this is not suitable for all situations. If we are talking about a large-scale long-term project, it is better to turn to professionals offering traditional QA services.

Based on our experience, we can identify several unique characteristics of such automation:

• self-healing thanks to the capabilities of AI and ML;

• the principle of using record and playback;

• standard functionality;

• integration with CI, and connection to the cloud.

Usually, such automation is performed on the basis of local technologies. But more and more popular are cloud solutions that are able to provide scalability, speed, continuity through collaboration, low risks at all stages of SDLC, and security.

In general, codeless automation testing tools are optimal for professionals, business users, and managers interested in automating routine repetitive processes that are most prone to errors. We prepared a list of tools that are not only simple and straightforward to use, but also as effective as possible.

Six Best Codeless Test Automation Tools

Katalon Studio

A comprehensive solution for AT apps (desktop, web, mobile) and APIs with a powerful set of features available for the non-technical person to understand and use. Dealing with this tool is quite simple for many reasons: user-friendly interface, intuitive operation of record, and playback, drag and drop, scaling without limits.

Features:

• Productive IDE on which a non-technical user can easily run tests for any platform and OS, regardless of the level of the app complexity.

• Easy to set up and use thanks to pre-built templates and combining all activities in one project.

• On-screen object management with the ability to view details about each, and reuse artifacts in the future.

• Flexible Modes: a novice can use recording and keywords to create ATs, while more experienced players can create advanced scenarios with full access to the IDE.

• Smart mechanisms including self-healing, smart waiting, scheduling, parallel execution, and native CI/CD integrations (Jenkins, Azure DevOpJenkins, etc).

Perfecto Scriptless

A Selenium-based platform designed for a wide audience from the IT: QA managers, architects, analysts, directors, and consultants. This full-featured tool is great for continuous and regression tests. It helps out in situations where you need to speed up scripting, avoid complex settings and do without the help of developers. Creating and running tests is possible in various work environments and platforms.

Features:

• Visual autotest creation using a drag-and-drop interface that anyone can handle.

• A self-learning engine based on ML that can detect patterns in tests and adapt models.

• AI-powered analytics to generate detailed reports every time you run.

• Autosave new items, allowing existing items to be reused for future streams.

• Cloud collaboration with the ability to share reports with QA and developers, receive error notifications.

• Integrations with Jenkins, TeamCity, and other CI/CD tools, built-in scheduler.

CloudQA

A cloud platform designed to reduce the time, cost, and complexity of the AT process. This solution does not need a test infrastructure. Non-technical users can create and run test scripts in minutes. Using the platform at the organization level saves money because you don’t have to invest in expensive toolkits and resources.

Features:

• Wide functionality, understandable for users with no scripting experience.

• Maintain integrations with Slack, GitHub, JIRA, and Travis CI to track bugs and improve work efficiency.

• Real-time performance and availability metrics to inform the user.

• The ability to record any action in a web app without the need to write code.

• Setting up notifications so that the platform instantly notifies you of identified problems.

Applitools

Powerful framework for visual UI regression testing. The peculiarity of this tool is that it is based on Visual AI. It can dramatically change the QA team’s approach to app testing, significantly increasing the speed and reliability of processes. Applitools Eyes helps you instantly find visual errors and flaws. Product testing can take seconds.

Features:

• Creating tests 5.8 times faster and detect 45% more errors (functional, visual regressions) than traditional AT, thanks to visual AI.

• Intelligent maintenance with automatic analysis of differences in all tests and report generation.

• Integrations with frameworks (Cypress, Selenium, etc.) using the SDC library, low-code tools (Testim.io, Tosca, Selenium IDE), SCM & CI/CD (Jenkins, GitHub, Gitlab, CircleCI), as well as with reporting solutions (Jira and similar).

• Dashboards with Smart Assist, which simplifies reporting, analytics, and management decision making within the project.

Usetrace

One of the codeless UI testing tools popular with SMBs with no coding experience. Automated test scripts are called traces here. Essentially, it is the user’s journey through the app using real browsers. The tool is valued for its speed, ease of use, ease of maintenance, and seamless integration with the development process. Overall, the tool is useful for both development and production.

Features:

• A toolbar with which users can launch a series of traces from a single interface.

• Detailed information for each browser session and deleted trace data, which is useful for recovery.

• Co-create UI tests as a team to speed up the AT process and simplify project management.

• Integrations with Slack, JIRA, Visual Studio Online, Jenkins, Flowdock GitHub, Codeship, HipChat, Bitbucket.

• Receive SMS alerts when a site goes down, which is a valuable feature for production.

Leapwork

A real find for IT users and businesses, which allows you to transfer routine, repetitive processes into automatic mode. The goal is to create optimal conditions for the implementation and scaling of automation. The company claims that this enterprise-grade platform helps improve productivity by 97% and reduce app errors by 90%.

Features:

• A common language for automation that bridges the skills gap between IT teams and business users.

• Short learning curve: From day one, the user can create and run tests with a simple constructor.

• Intuitive process design without having to type lines of code.

• Collaboration of specialists from the same field, that eliminates tedious, repetitive tasks.

• Built-in reusability, which helps reduce the burden on QA teams.

Codeless automation emerged as a response to shortcomings in the QA world, mainly related to the Agile development life cycle. The new approach made possible process continuity and the improvement of quality at every stage. Many of the tools are based on advanced AI and ML, bringing a new level of bug detection, analytics, and reporting.