Latest QA Trends: Key Insights Into Test Automation

by Oleksii Tkachuk | December 19, 2024 1:53 pm

Test automation is still one of the most confusing quality assurance approaches. Even though it’s been around for decades, there are still many myths and misconceptions that lead to low efficiency, quality issues, and wasted resources. Each year, thousands of questions about test automation are searched online or asked on platforms like Quora and Reddit.

Considering that the majority of queries about automation are caused by a lack of understanding, we’d like to shed some light on the ones we’ve heard the most this year. Let’s omit stuff like “Will we have to fire our manual engineers?” or “It’s too expensive, what are the benefits?” – these are based on the myths we’ve already busted previously[1]. In this article, let’s answer the most substantial questions clients asked in 2024 and establish a strong foundation for quality assurance in 2025.

What’s Next for Test Automation?

It’s legit to expect that in 2025, advancements in autotesting will build up to a newer, more efficient process. First of all, AI and ML will play a bigger role, enhancing test maintenance, optimization, and case generation. The available toolset already greatly improves the margins and productivity of automation, while further progress will continue this trend.

The shift-left approach to testing also plays an important role in the results brought by test automation. The shift-left methodology emphasizes the early integration of testing in the development lifecycle. Testing during the requirements and design phases, with developer involvement, enables earlier defect detection, significantly reducing fixing costs. For example, a study by the Systems Sciences Institute at IBM shows that fixing bugs early can save up to 100 times the cost compared to post-release fixes. Learn more at the following link[2].

Test automation frameworks are being significantly transformed by recent shifts in development approaches. Broad adoption of continuous integration and continuous deployment (CI/CD), the transition to microservices architecture, and the demand for faster feedback loops are reshaping their current capabilities. Moving forward, we can expect deeper integration of hyperautomation and cloud computing to further enhance test analytics, reduce manual intervention, optimize test stability, and create a more adaptable approach.

Will AI Take Place of Automation Engineers?

One of the biggest concerns in recent years is AI replacing human workers. However, as technology evolves, it becomes increasingly evident that AI is a powerful tool for enhancing employees’ efforts rather than replacing them. In automation, AI transforms how test scripts are created, executed, and maintained. Through intelligent test generation, self-healing mechanisms, and predictive analytics, AI helps to create more resilient automated tests, accelerate execution, and simplify debugging and maintenance processes.

AI and ML improve workflows and resource allocation, delivering faster results and cost savings. However, the strategic input of QA specialists remains essential for guiding these tools to achieve maximum value. Companies are already seeing the results: faster delivery times and significant cost savings. Curious about the actual ROI? We explore the topic in detail in our recent article[3] about AI in test automation. Make sure to check it out.

What Can Be Covered With Automation?

It’s important to understand that 100% automation is unachievable, and some testing requires human perception. Well-crafted approach is a synergic process comprised of two parts: a manual input based on personal experience and perception to address the needs of end-users; and automation of routine tasks to optimize efficiency and increase test coverage.

The main focus areas for test automation are:

• Regression Testing: Ensures updates do not disrupt existing functionality.
• Integration and API Testing: Validates internal and external module interactions.
• Performance Testing: Assesses scalability, latency, and stability under load.
• Core Functionality Testing: Confirms the reliability of critical features.
• Unit Testing: Verifies individual functions early in development.

According to the “2024 State of Testing Report”[4] by PractiTest, 30% of respondents admitted to automating about 50% of their manual effort, growing from 25% in 2023. In the meantime, only 2% completely replaced manual testing, which is down from 3% in 2023. This data highlights that comprehensive test coverage is about the synergy of manual and automated work.

Which Tools Are Required to Start Test Automation?

One of the main issues leading to test automation failures is selecting the tech stack. In their research on automation[5], AIMultiple outlines that nearly 26% of companies surveyed have indicated that finding the right test automation tech is the biggest challenge they face. And let’s be honest – with so many options out there, each with its own mix of pros and cons, it’s no wonder teams struggle with this decision.

To make sense of it all, we’ve already explored the topic of tools for automation in a dedicated article[6]. It breaks down everything you need to know about test automation tools based on solutions like Katalon Studio, Selenium, and more. The article also includes a handy comparison chart to show you exactly what each of them can (and can’t) do. Make sure to explore whether you’re struggling with the decision or simply exploring test automation.

How do I Launch Automated Testing?

As automation is a demanding approach that requires careful preparations to foster results, the first step is to ттyour project and check whether it has matured enough. If you have a stable, complex product that is regularly updated and requires a lot of repetitive re-checks – it might be the right moment. Automation will allow you to save time, speed up the testing process, and optimize expenses. Otherwise, think twice if you’re ready, as according to KMS[7], nearly 64% of test automation projects fail to meet expectations if launched prematurely.

As soon as your project is at the right point for automation, follow these six steps to ensure a successful start:

• Step 1 – Define a Testing Strategy
  Outline objectives, scope, communication channels, tools, risks, and mitigation plans to align automation with project goals and resources.
• Step 2 – Set Up a Test Environment
  Configure the software, database, network, and compatibility stack (browsers, OS, hardware) to simulate real-world conditions and detect errors effectively.
• Step 3 – Develop and Maintain Test Scripts
  Create detailed, reusable scripts starting with simple cases, updating them consistently to match development changes for regression testing and process consistency.
• Step 4 – Integrate Tools and Frameworks
  Use compatible tools like Selenium, Cypress, or Appium to seamlessly connect with CI/CD systems, issue trackers, and test management platforms.
• Step 5 – Run and Support Automated Tests
  Continuously execute frameworks, address issues, and enhance tests to adapt to product updates while maintaining a robust testing process.
• Step 6 – Analyze and Report Results
  Track performance, identify improvement areas, and use detailed reporting to streamline onboarding and refine testing strategies.

You can find a more in-depth guideline along with the “When To Automate Testing Process” checklist in our article at this link[8].

What Else to Expect Soon?

If we were to fantasize about other advancements that could be seen in the not-so-distant future, it would be the following technologies. Edge computing and IoT testing will become increasingly important, requiring new automation approaches for testing distributed systems and smart devices. Frameworks will need to adapt to handle real-time data processing, validate complex device interactions, and maintain consistent performance across diverse network conditions.

Security testing automation is another area poised for significant advancement, driven by increasing cybersecurity concerns. We’ll likely see more sophisticated automated tools for vulnerability scanning, penetration testing, and compliance validation integrated directly into CI/CD pipelines. As blockchain technology becomes more widespread, testing challenges such as validating smart contracts, transaction integrity, and distributed ledger consistency will rise in importance. Additionally, the rise of quantum computing may introduce new testing challenges and opportunities, particularly in areas requiring complex calculations and cryptography validation.

Anticipating 2025

As we look ahead to 2025, test automation will continue its gradual expansion with growing efficiency and nods toward hyperautomation. Deeper integration of AI and ML will simplify test maintenance, improve analytics, and significantly reduce manual effort while increasing productivity. At the same time, the shift-left methodology will push testing earlier in the development lifecycle, enabling faster issue detection and resolution, which will lead to substantial cost savings.

However, while the expansion of AI’s role and value is inevitable, it’s clear that human QA expertise will remain irreplaceable, ensuring tech is effectively guided toward meaningful results. Test automation will continue to focus on areas like regression, API, and performance testing, making it an indispensable part of any modern QA strategy.

The tech stack will also continue improving with test frameworks, aiming for enhanced scalability, stability, and analytics. However, the ongoing challenges of selecting the right stack and evaluating project readiness will remain critical. With better resources and a clearer understanding of when and how to launch automation, businesses can maximize their potential to achieve high-quality software in 2025.

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Whether it is uncharted territory for you or your team already has some background in automation (it can be positive as well as negative), QATestLab can help you navigate this path efficiently. Request our free project estimation[10], and we’ll assess your project to provide tailored recommendations on test automation.

Related Posts:

• AI In Test Automation: From Costs To Benefits[11]
• No-code Solutions = No More Guarantees[12]
• Testing of MVP: What Is the Benefit for Startups?[13]

Source URL: https://blog.qatestlab.com/2024/12/19/test-automation-insights-and-trends/

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