With the advent of Agile in software development, there is a need to release products quicker than before. Today, automation testing is a vital part of the software development process, widely used by organizations of all sizes to enhance the quality, speed, and affordability of their software development. The released products must be of good quality, which requires efficient and effective testing methods. Luckily, there are multiple areas where testers can benefit from various process automation.
Earlier, test scripts were written from the developer’s perspective to check if different components were functioning together correctly. And while that’s precisely how some tests should be written (unit tests, for example), we do want a different approach for end-to-end testing. We want e2e tests to operate entirely on the visual layer, since this is what the end user interacts with.
Automation testing continues to evolve to keep up with the latest technological advancements. Let’s take a look at the current trends in the market and some QA tools that are expected to enhance your automation testing process in 2023.
Testing trends in 2023
As opposed to before, QA is no longer just about finding bugs but also preventing them. For every stakeholder involved, be it a product owner outlining requirements, or a developer writing code, the responsibility of quality assurance is a joint effort.
With quality being ensured across a spectrum of roles, it is necessary to have tools that help get testing done hassle-free. Due to this, trends in the technology industry are now catering to making the experience of using QA tools more straightforward.
Below are some trends that are likely to gain more traction in the upcoming years as the software testing market grows.
Emphasis on customer experience
Every organization has a vision of how its product will look and behave. But for a business to do well, the focus also needs to be kept on market trends and customer satisfaction. As the product grows, it is imperative to maintain that balance between these two factors so that the organization stays competent in the business.
Demand for no-code and low-code applications
These tools utilize AI and visual modeling to accelerate and simplify test case creation and test runs. These tools require minimal coding knowledge and help engage manual testers and other subject matter experts in the quality assurance process.
AI and ML in QA
Having these technologies in QA tools has dramatically increased the performance and productivity of QA engineers. Some ways in which AI and ML help are
- Self-healing capability in automation test scripts
Self-healing of automation scripts refers to automated test scripts’ ability to detect and fix failures without manual intervention. If a test script fails because of a change in the UI, a self-healing automation system can automatically detect the change, modify the test script accordingly, and continue testing. This helps in reducing the downtime caused by test failures and improves the overall efficiency of the testing process. For example, if the test script is expecting the id of the login button to identify the button on the screen and this id is changed, then it will rely on other attributes of that button to locate it and proceed ahead.
- Test data generation
AI algorithms can be used to automatically generate test data based on predefined criteria and constraints, making it easier to test applications with a large number of possible inputs.
- Test case prioritization
ML algorithms can analyze test results, historical data, and usage patterns to optimize test execution, reduce the number of tests run, and improve overall testing efficiency. If multiple tests are to be run in parallel, prioritization can help identify which tests should run first.
QAOps and Agile
QAOps is an emerging practice that integrates QA into the software delivery pipelines. It encourages the QA team to work directly with the operations and development teams. This helps to have a wholesome outlook and better quality of the end product.
Hyperautomation combines automation tools like AI, ML, Natural Language Processing (NLP), and Robotic Process Automation (RPA) to leverage the advantages of AI-driven decision-making. Organizations will likely utilize hyperautomation more to improve productivity and save on costs.
Using cloud-based platforms tends to give organizations the ability to scale and grow as and when required. Many upcoming QA tools are leveraging this technology.
Characteristics of a good test automation tool
A good quality assurance tool should have the following characteristics:
- Ease of Use: the tool should be easy to use and have a user-friendly interface that makes it accessible to users with varying levels of technical experience.
- Integration: the tool should integrate seamlessly with the existing development environment, including continuous integration/continuous delivery (CI/CD) pipelines.
- Scalability: the tool should be scalable to meet the needs of organizations of different sizes, from small startups to large enterprises.
- Reporting: the tool should provide robust reporting and analytics capabilities, enabling users to monitor and evaluate test results in real-time.
- Customization: the tool should allow for customization. This includes the ability to integrate with other tools or plugins. Since each organization has its specific needs and workflows, the QA tool should be able to adapt to that.
- Collaboration: the tool should facilitate cooperation between team members, enabling them to share test assets and results and work together more efficiently.
- Security: the tool should be secure, with robust data protection and privacy features to ensure the confidentiality of sensitive information.
- Support: the tool should come with comprehensive documentation and support, ensuring users have access to the resources they need to succeed.
By focusing on these critical characteristics, organizations can ensure that they select a QA tool that will meet their testing needs and support their development workflows over the long term.
Types of automation testing
Each type of automation testing has its own specific goals and objectives. Organizations may use a combination of different types of automation testing to ensure that their applications meet their quality and performance requirements.
- Integration Testing validates how different components of an application interact with each other to ensure that they work correctly as a system.
- Functional Testing tests the application against its functional requirements to verify that it works as intended.
- Regression Testing ensures that changes or additions to the application’s functionality do not break existing features.
- End-to-End Testing tests the entire user flows, from start to finish, to ensure that the application works as a complete system.
- Acceptance Testing tests the application against acceptance criteria, typically set by stakeholders, to determine if it is ready for release.
- Performance Testing validates the application’s performance and response times under various conditions, such as heavy load, to ensure that it will meet performance requirements.
- Security Testing is responsible for the application’s security features, such as authentication and encryption, to ensure protection against security threats.
- User Acceptance Testing (UAT) verifies the application with real-world users to determine if it meets their needs and expectations.
Top 5 QA tools to look out for this year
With a plethora of QA tools out there in the market, picking the right one to satisfy your requirements might be tricky. Especially when the claims made are not always delivered up to the mark. However, below are the top 5 tools that are likely to make testing better in their respective domains.
Lambdatest is a cloud-based, cross-browser testing platform that allows users to test their websites and web applications across a wide range of browsers, operating systems, and devices. The tool is meant to make cross-browser testing fast, easy, and efficient. Its features are aimed at helping organizations improve the quality of their websites and web applications and reduce the time and resources required for testing. It supports real-time testing, parallel testing, mobile testing, and integrations with other tools and platforms.
testRigor is a cloud-based end-to-end testing tool. It supports web and mobile browsers, native desktops, hybrid and native mobile applications, and APIs. The best part is it is a codeless system, meaning that you can write tests from scratch in plain English commands. testRigor uses AI algorithms, allowing up to 15x faster test creation and 200x less test maintenance. The tool integrates with many other tools like Jira, Zephyr, Oracle, PostgreSQL, and CI/CD tools like Jenkins, AWS, and GitLab – to name a few. testRigor also provides a record-and-playback extension that you can leverage to record test cases and generate test scripts in plain English.
Qase is a test case management platform that is designed to help teams manage their testing efforts, automate testing tasks, and improve the efficiency and accuracy of their testing workflows. Using Qase, you can have a centralized solution for managing your test cases, integrating with other tools and reporting and analytics features.
Applitools is a cloud-based, visual AI testing platform for web and mobile applications. Visual AI groups together bugs that have similar traits, like those that occur across different screen sizes, browsers, devices, or other components, making test maintenance an easily automated task. Visual AI doesn’t require a DOM or schema structure; hence, you can test across mobile and web apps. It is designed to help teams automate their visual testing process, ensuring that their applications look and function as intended across a range of devices and platforms. Applitools integrates with popular test frameworks, including Selenium, JUnit, and more, enabling teams to automate their visual testing process as part of their existing workflows.
Mockaroo is a cloud-based platform for generating fake data for testing purposes. It allows software developers and testers to quickly and easily generate large amounts of fake data for testing applications, populating databases, and more. It offers features like customizable data generation, download and export options, API access to the generated data, and large data sets.
In conclusion, automation testing is a crucial part of the software development process, and its importance is only growing. The advancements in technology and the introduction of new trends have led to QA teams shifting the way they used to operate. In 2023, it is important to stay updated with the latest tools that can enhance your testing process, improve the quality of your software, and ultimately benefit your business. Some of the tools to look out for in 2023 include AI-powered testing, cloud-based testing, and tools that integrate with Agile development processes. Stay ahead of the curve by incorporating these tools into your testing strategy and reap the benefits of a streamlined and efficient software development process.