In an age when customer experience can make or break a software product’s success, newer technologies effectively test software must be on every enterprise’s radar. As it stands, automation is clearly the future of software testing, facilitating a more efficient and reliable testing process. Now, with the advent of AI and Machine Learning, we can expect significant transformations in the field, unveiling new possibilities and avenues in the realm of software testing. The potential for automation in various industries is highly diverse, with healthcare having a maximum adoption rate. This stark rise in the adoption of automation testing can be attributed to an increasing number of companies recognizing its benefits.
This blog covers the latest trends in automation testing and gives an overview of what the future of automation testing might entail.
Technologies Driving the Evolution of Software Testing in an Economic Downturn
The software testing industry is on the cusp of major advancements, driven by the integration of automation tools, codeless automation, and AI/ML. In today's economic climate, organizations are looking to cut costs which can further harm app quality and brand reputation. Automated testing tools provide a solution to these challenges by allowing testers to speed up release cycles, reduce human effort, and improve the efficiency, accuracy, and reliability of testing, all while addressing financial constraints.
Companies nowadays are increasingly using automated testing methods to improve their testing processes and deliver better customer satisfaction. Manual testing can be slow and lead to inferior product quality, longer release cycles, and a poor customer experience. In contrast, codeless automation testing eliminates the need for manually coding test cases, making the testing process more agile and efficient.
Now, AI/ML-powered testing tools that can predict test scripts and adjust to specific web page requirements are looking to further ensure testing efficiency and accuracy.
The way test cases are written is disrupted by the onset of the following methodologies:
- Keyword-driven codeless frameworks: This approach uses a table format to define keywords or action words to carry out a function.
- Behavior-driven framework: This method involves writing tests using plain, descriptive English grammar.
- NLP-driven frameworks: This approach allows users to write test steps using natural language, typically aided by AI-powered natural language automation and a next-gen recorder.
Among these, keyword-driven and NLP-driven frameworks have been the most impactful, making it easier to create test cases using simple keywords and natural language. They also help improve decision-making by extracting insights from unstructured data.
Exploring the Top Automation Testing Trends in a Challenging Economic Climate
To have a better idea of what test automation will look like in the future, let's explore some key trends that experts predict will shape the future of software testing.
1. Сloud-based Testing
Cloud-based infrastructure is becoming increasingly popular among quality assurance engineers, allowing them to access a pool of devices that emulate real-world traffic and environments. This helps them run automated tests more efficiently and eliminate server configurations and deployment issues.
Check out: Why is Cloud Automation Testing gaining popularity?
2. Continuous Testing
This approach involves testing at every stage of the development process in order to catch issues early and to get quick feedback about the code. Automated testing tools are commonly used in continuous testing to help make the process more efficient.
DevOps and CI/CD integration are two trends that are supported by continuous testing. In 2023, early testing—that too with the aid of automation—will continue to be widely used.
3. Codeless Automated Testing
The automation testing future is trending towards codeless methods, increasing testing efficiency, reliability, and stability. Eliminating the need for writing thousands of lines of code, this method instead uses a tool-based interface to create automation suites. A variety of codeless automation testing tools are available with specific features dedicated to UI testing, end-to-end testing, and more that offer great flexibility by creating reusable test cases.
Some benefits of codeless automation testing include the following:
- Quick set up and simple to use
- Reduces the automation costs significantly
- Provides flexibility for reusing test cases
- Accelerates the delivery of quality
4. AI & ML
Artificial Intelligence and Machine Learning have become quite the staple in automation testing. Using advanced machine learning algorithms, AI can optimize test cases by learning from previous executions. Software testing teams are increasingly employing AI/ML technologies to organize, prioritize, and optimize tests in order to enhance test coverage and carry out specific tests.
As AI-driven test management technologies become more ubiquitous, developers can automate test reporting, scheduling, and prioritizing. Additionally, utilizing an AI-driven test automation platform enables them to simultaneously execute several test cases on an application, ultimately leading to saving time and costs.
According to predictions, testing processes will see an uptick in AI usage, especially for analytics and reports such as:
- Optimization of Test Suites: Identification and elimination of unnecessary test cases.
- Log Analytics: Finding exceptional test cases involving both manual and automated testing.
- Predictive Analytics: Prioritization and streamlining of testing activities based on user needs.
- Test Needs Coverage: Identification and utilization of essential keywords from the RTM (Requirements Traceability Matrix).
- Defect Analytics: Detection of bugs and defects that may pose risks.
5. IoT Testing
The penetration of IoT devices across business operations has been quite rapid. Consequently, it has become important to test IoT devices for functionality, usability, performance, connectivity, security, and compatibility. IoT testing is quite a complicated arena within the domain of Software Testing, as these applications involve sensors, hardware, software, and databases. Apart from assessing the compatibility of device versions and protocols, it will be equally important to evaluate data integrity and provide for safety evaluation. Automation of IoT tests will be a major point of focus for enterprises this year.
6. Exploratory Testing
As automation testing helps take care of repetitive tasks, QA will have more bandwidth for exploratory testing. The growing importance of Agile & DevOps in development and testing departments will make exploratory testing inevitable in the future of automation testing. Automation testers will be expected to find unique test cases around their web applications, using scenarios unnoticed by scripted testing.
7. In-sprint Test Automation
The future of software testing will witness the continued use of in-sprint test automation, a methodology that allows for testing during the development process rather than waiting until the end. This approach is becoming increasingly popular due to the demands of the agile methodology, which requires the quick release of new versions. Traditional methods, such as regression and DDT, can lead to bugs slipping through to production, but in-sprint test automation helps maintain software quality and keeps the testing team in sync with the development process.
Also, in-sprint test automation can prevent organizations from incurring high costs associated with releasing a product with bugs. Identifying and addressing bugs before deployment is much more cost-effective than fixing them on production, which can cost up to 100 times more. Implementing in-sprint automation testing also helps maintain brand reputation and end-user trust and saves financial and human resources.
8. Automated Regression Testing
Regression testing prevents new code from breaking existing functionalities and provides a safety net to find regression bugs. This test helps deliver efficient solutions with enhanced scalability and speed.
Automated regression testing tools include Selenium, TimeShiftX, IBM Rational Functional Tester, SilkTest, TestComplete, Ranorex, etc., which provide advantages like effective bug detection and high ROI.
Among the most recent developments in software testing, DevTestOps, will continue to play a vital role in the future of automation testing. While DevOps helps organizations deploy system changes efficiently and quickly, DevTestOps enhances deployment quality through continuous and automated testing. By using DevTestOps, bugs are identified before the final release of the software, thereby improving the customer experience.
A growing number of businesses are working to provide end-to-end solutions as a result of the expanding need for AI-powered automation testing. However, the fundamental issue here is that none of the enterprises individually have all the resources necessary to accomplish this.
How Can HeadSpin Assist You in Transitioning to the Future of Software Testing?
There’s no getting around the fact that test automation is faster and more efficient than manual testing and can help test software more effectively. HeadSpin offers a cloud-based Platform to test software on multiple devices at once, which helps developers efficiently automate testing processes, saving time, costs, and resources. The HeadSpin Platform can also integrate ground-breaking test automation frameworks like Appium or Selenium and automate intricate user journeys. It also allows for the automatic checking of new builds, performance regression testing of apps/websites, and CI/CD integration features.
Here are some unique benefits that HeadSpin provides businesses looking to make the most of test automation:
- Identifies the underlying causes of problems, such as hung and slow methods and bloated third-party SDK
- Analyzes performance across numerous devices or OS to determine compatibility and performance across devices and OS
- Ensures creating unique KPIs and assesses them under numerous circumstances with the help of HeadSpin's data science capabilities
- Provides test scenarios that use third-party services or applications to simulate real-world situations
- Runs tests using real devices deployed in 90+ locations worldwide, using HeadSpin's Global Device Infrastructure
The Way Forward
The software testing future will continue leaning more and more into automation. And when it comes to test automation, adopting the right approach and the best practices can yield significant benefits, including cost savings, increased team productivity, and user-friendly applications. If you're looking for a partner to help set up automation testing for your company, consider HeadSpin, an AI-driven Platform that can easily integrate with your current frameworks, to enhance your automation testing processes, capture important performance metrics, and improve your end-user experience.
Utilize HeadSpin to future-proof your automation testing and deliver your customers high-caliber software.
Q1. What is the foremost upcoming breakthrough in software testing?
Ans: The future of software testing is automation, no-code, and AI-assisted testing. Although there are conflicting opinions about these technological advancements, automation will dramatically alter and simplify testing for everyone.
Q2. Which type of testing is in most demand?
Ans: Agile has been embraced by organizations in response to rapidly shifting requirements, and DevOps has been adopted in response to a need for speed. With the advent of NoOps and DevTestops, the testing landscape may soon shift.
Q3. What does the future of automation testing look like?
Ans: Automation testing has a strong future outlook. Quality assurance teams will still need to validate the output of automation tools while the tools are used to create test cases and code tests. This means that the growth and development of automation testing will continue for some time.