Guest Blog by : Mit Thakkar, KiwiQA Introduction Software testing is an integral process of the software development lifecycle. The online business landscape is evolving continuously, and software developers cannot compromise on speed. Developers and software development firms must...

Guest Blog by : Mit Thakkar, KiwiQA

Introduction

Software testing is an integral process of the software development lifecycle. The online business landscape is evolving continuously, and software developers cannot compromise on speed. Developers and software development firms must focus on consistent and fast release of products to the market.

At the same time, the software testing process has to be repeated multiple times to ensure quality of software solutions. In such cases, automation testing comes to the rescue by helping developers maintain pace with the emerging trends for agile development.

Automation testing focuses on using software tools and scripts for executing pre-defined test cases and automatic verification of outcomes in specific use cases. The importance of automation testing revolves around improving the speed, efficiency, and accuracy of the software development process through the automation of repetitive and time-intensive manual testing processes.

During the 1990s, the web browser concept gained attention. Software testing was primarily a manual task that involved the use of ad-hoc methods for checking errors. Test automation became a reality only in the 2000s, with the rising prominence of Selenium and WebDriver projects for tackling multi-device and cross-browser testing challenges. The two projects became one in 2011 with the creation of Selenium WebDriver, which subsequently turned into a W3C standard in 2018.

One of the earliest practical implementations of automation testing techniques can be traced to the Automated Test Engineer or ATE system by IBM in the 1970s. Over the years, automation testing techniques have evolved from the traditional record and playback models to AI and ML. The evolution of automation testing techniques started with the introduction of functional and regression testing.

It also focused on data-driven and keyword-focused training alongside test automation frameworks. The recent stage in the development of automation testing points to the growing preference for agile and DevOps. AI and ML have also emerged as promising tools for revolutionizing automation testing.

Why Automation Testing for Web Platforms is Crucial?

Automated software testing services is an integral necessity for web platforms as it could help in adapting to the dynamic nature of web technologies. The evolution of web technologies calls for radical shifts in the quality and usability of software solutions for the target audience. Therefore, automation testing could help in the accurate and efficient evaluation of errors before deploying web platforms.

Increased Need for Speed in Software Development Cycles

Software development is no longer a unique initiative restricted to a few elite companies. Every business wants to make an impact in the continuously changing dynamics of the online business environment. Automated testing could help in increasing the speed of software development cycles alongside ensuring accurate identification of errors.

Delivering a Seamless User Experience

The most important value advantage of automated software testing is the potential for improving software quality by expanding the range of tests. Automated testing could help in running multiple use case scenarios simultaneously, which helps in identifying errors that could be unnoticed in manual testing. As a result, automated testing could help in creating solutions that offer a seamless user experience.

Traditional vs. Cutting-Edge Automation Testing Techniques

The journey of traditional automation testing techniques started with recording and playback tools. Subsequently, functional and regression testing evolved to check the functionality of the software and the impact of new changes on existing functions. Then, data-driven and keyword-based testing showcased the benefits of using scripts for creating tests. Gradually, the traditional methods gave way to the rise of test automation frameworks and AI and ML-based testing tools.

Why The Shift to Cutting-Edge Techniques Is Essential?

The shift to cutting-edge automation testing techniques is essential for adapting to the emerging changes in the technology landscape. New web technologies and emerging trends in consumer behavior have spurred massive modifications in the world of technology. As the demand for seamless user experiences continues building up, it is reasonable to think of cutting-edge automation testing techniques that can keep pace with emerging trends.

Latest Automation Testing Techniques

The automation testing market could achieve an impressive annual growth rate of 15% over the period from 2023 to 2032. With a market capitalization of more than $20 billion USD, it is clearly evident that the global automation testing market has been expanding at an impressive pace.

Latest automation testing techniques, such as scriptless automation testing, blockchain testing, unified test management platforms, platform engineering, and API simulation, have gained considerable popularity in recent times. Let us take a look at some of the prominent automation testing techniques that have made a formidable impact now.

1. Visual Validation Testing

Visual validation testing or visual regression testing is a process of checking whether specific visual aspects of the user interface of an application are suitable for the users. The primary aim of visual validation testing is the verification of the correctness of content and data displayed on the front end of an application. Furthermore, visual validation also verifies the layout, appearance, and presence of visual elements on the user interface as well as the whole UI.

Tools And Technologies for Visual Validation

Some of the popular tools used for visual validation include Kobiton, Lambda Test, Applitools, Chromatic, Percy (Browser Stack), and SMARTBEAR. Apart from these premium tools, you can also explore free, open-source tools such as BackstopJS, Depicted, Viff, and CSS Critic.

Benefits and Challenges

The most promising advantages of visual validation testing include faster detection of bugs, code reduction, faster implementation, and lower workload for testers. In addition, designers would find valuable feedback which can help in improving UI quality. However, visual validation testing also comes with certain challenges, such as inconsistencies among different testers. On top of it, the need for maintaining the automated test cases can impose a formidable challenge for visual validation.

2. AI and Machine Learning in Test Automation

AI could help in producing automated tests with more precision. The most important role of AI in improving test automation is the training phase. During the training phase, machine learning models would go through training on a particular organizational dataset. In addition, some tools also feature pre-trained models updated on a frequent basis by utilizing continuous improvement for specific applications. The advantage of AI-powered test generation is that it helps in developing a test plan according to the desired use cases and strategy.

Predictive Analytics in Testing

Artificial intelligence and machine learning could utilize predictive analytics to identify different features for testing. In addition, it also helps in creating specific test plans without human intervention. On top of it, automation testing teams could leverage ML and AI to improve their testing strategies. Predictive analytics could offer insights for optimizing test suites through the removal of unwanted test cases and emphasizing optimal test coverage.

Adaptive Test Maintenance

AI and machine learning also offer the advantage of adaptive test maintenance, as developers and testers could find faster feedback reports on the working of test cases. Machine learning or AI could help in increasing the overall depth and scope of tests without affecting overall improvements in software quality. Artificial intelligence helps in improving test automation by removing the need for continuously tracking the changes. AI could support the auto-correction of tests to an extent, thereby ensuring the maintenance of all tests in one click.

3. Containerization and Test Automation

Docker is a popular software platform for building, testing, and deploying applications at a fast pace. It can help in packaging software into different standardized units, such as containers, which have all the components, such as libraries, runtime, system tools, and code. Containers are highly customizable software-defined environments that could host applications or services and have a formidable influence on software testing. For example, Docker can help in leveraging a combination of parallelization and automation to solve the problems emerging due to a lack of time and resources.

How Containerization Enhances Test Consistency?

Containerization helps in making software testing more consistent and predictable. It focuses on using the same environment, in the form of a container, for hosting your software during the testing process. The consistent application environment for software testing ensures consistency as the tests would show exactly how the application would run in production. You can find small differences between the container-based testing environment and the deployment environment. However, containerization helps in restricting the differences to a minimum level.

Containerization in automation testing could help in packaging all the dependencies and variables of an application in a single container. In addition, containers can also promote scalability by offering the flexibility for maintaining multiple test branches. Docker allows you to scale automated testing setups by spinning up or removing containers according to the requirements. Scalability serves as an essential factor for evaluating the performance of an application under different load scenarios.

4. Parallel and Distributed Testing

Parallel testing is the process that involves concurrent testing of multiple applications or different components of one application. The distinct highlight of parallel tests is that they contain two or multiple parts for checking separate features of applications. The different parts of parallel tests are executed simultaneously on independent computers. Furthermore, parallel testing could support a significant reduction in test time alongside increasing the efficiency of the testing process.

Tools for Distributed Test Execution

The popular testing tools for distributed testing include Selenium Grid and Apache JMeter. Selenium Grid is a powerful automated testing tool that supports distributed test execution alongside increasing the pace of the testing process. On the other hand, JMeter supports distributed testing with flexibility for running test plans across different systems and servers concurrently.

Speeding Up Test Cycles

Parallel testing could help speed up test cycles by ensuring easier management of workload and execution of test cases across different systems. In addition, distributed testing also removes the concerns of traditional testing methodologies by offering freedom from dependency on a single machine. Most important of all, parallel testing allows interaction between multiple test cases, which could lower the overall testing time.

5. Behavior-Driven Development (BDD) and Testing

Gherkin is a plain-text programming language with a simple structure that provides ease of learning even to non-programmers. However, the structure of Gherkin also enables a brief description of test scenarios alongside examples for highlighting business use cases in real-world domains.

It helps Cucumber developers define tests that could help product teams describe the requirements for new products. All the features in Gherkin are specified in a file and follow strict syntax. The syntax for the Gherkin language provides the foundation for Behavior-Driven Development or BDD.

Tools For Behavior-Driven Development

Cucumber is one of the popular tools in the domain of software testing, which supports Behavior-Driven Development or BDD. It can ensure promising improvements in software quality alongside reducing maintenance costs. On the other hand, SpecFlow works for defining, managing, and executing human-readable performance tests for .NET projects. It also supports BDD by creating comprehensible documentation for the software development lifecycle.

Collaborative benefits of BDD

BDD testing focuses on developing software that could address the requirements of all stakeholders alongside ensuring the highest quality standards. It encourages a collaborative approach, which is essential for modern software development methodologies. BDD testing could support collaboration between developers, testers, and business stakeholders. As a result, everyone in the development lifecycle could follow the business goals for creating an ideal solution.

How To Choose the Right Web Platforms For Automation Testing?

Factors to Consider

As the software market evolves with new demands, industry players should adapt to the emerging demands of quality. However, you should choose the right web platforms and tools for automation testing according to different factors. You must pay attention to different factors, such as compatibility of the platform and scalability.

Platform Compatibility

First of all, you must look for a web platform or testing tool that is compatible with different types of platforms and devices. You should find a testing tool or platform that helps you check the functionality and performance of an application on different platforms.

Scalability

You should look for a web platform for automation testing that offers the advantage of scalability. Automation testing platforms should offer the flexibility for adding or reducing resources required for testing according to your requirements. For example, Docker can support containerization, which allows easier modification of resources.

Community Support

One of the most distinctive highlights of a reliable web platform for automated testing is the assurance of community support. You must choose a platform that has an extensive community of developers and testers ready to share their knowledge with you. The availability of community support ensures that you don’t have to deal with every challenge in automated testing on your own.

Cost-effectiveness

Above everything else, you should pay attention to the cost of using the web platform for automated testing. You should check whether the web platform offers the desired functionalities for automated testing within your budget. On top of that, you should look for free trial versions to check the functionalities of an automated testing platform before choosing it for your development lifecycle.

Challenges in Implementing Cutting-Edge Techniques

Initial Setup and Learning Curve

The biggest challenge in the implementation of cutting-edge techniques for automated testing is the initial setup. Without any prior experience with a specific platform or tool for automation testing, you are likely to face confusion in setting up the tool. At the same time, your development and testing team would have to learn about the functionalities and settings of the tools and platforms before using them. Therefore, awareness and seamless onboarding could help in overcoming the initial friction for implementing cutting-edge techniques.

Integrating With Existing Systems

Another problem in the adoption of cutting-edge techniques for automated testing is the integration with existing systems. It is important to choose automation testing tools and platforms that offer compatibility advantages. On top of it, comprehensive training could also help in overcoming the barriers to integrating automated testing tools with existing systems.

Conclusion

Developers and testers should stay updated with the latest automation testing techniques to capitalize on the benefits of improved testing mechanisms. For example, new automation testing techniques could provide scalability and better scope for test coverage. In addition, automation testing techniques also boost software testing with new approaches like parallel or distributed testing. At the same time, you can also notice the impact of new technologies, such as AI and ML, on improvements in automation testing.

As the demand for automation testing techniques continues gaining momentum, you should prepare for the challenges that lie ahead. You can invest in continuous learning by exploring new tools and techniques for automation testing. If you want to prepare for the future of software testing, you should start learning more about automated testing right now.

Written By:

Mit Thakkar, Marketing Head at KiwiQA, offering Automation testing company. He drives the industry forward with his passion for excellence, crafting marketing strategies that effectively communicate the value of reliable software testing to businesses worldwide.

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