Automation has taken the software industry by storm. It has significantly transformed testing processes, improving efficiency, accuracy, and time-to-market. Automation may have once been one of the most popular trends in the industry.
However, today, it serves as a foundation for robust software delivery.
In this ever-evolving technological era, new trends in the software testing landscape keep emerging. Therefore, enterprises must do everything possible to stay ahead of these trends and gain a competitive edge.
In 2025, which software testing trends will stand out?
In this blog post, we dive deep into that.
But before we start, let’s get something out of the way.
Enterprises strive to achieve maximum efficiency in the testing process. But this journey isn’t as straightforward. There are a few hiccups in the enterprise testing strategy to be aware of — even in 2025.
Security is paramount in this day and age. As cyber threats evolve, staying ahead of potential risks could be daunting. Frequent and thorough security testing is essential to uncovering and immediately addressing vulnerabilities to avoid data breaches.
Test automation contributes massively to testing success. However, maintaining automated test scripts remains a key challenge for enterprises. Any modifications in the app’s functionality or UI also require script changes. Therefore, enterprises need a solid strategy to streamline script updates.
Real device testing? It helps understand the behavior of websites and web and mobile apps in real-world scenarios. However, setting up, configuring, and managing multiple real devices is time-consuming, expensive, and often intimidating for enterprises.
Modern software solutions have complex architectures with numerous dependencies. Comprehensive test coverage is crucial, but ensuring it for such intricate systems still poses a significant challenge.
The following ten trends in automated software testing empower enterprises to optimize resource utilization, enhance efficiency, and quickly roll out new features.
This is also known as codeless test automation, and it eliminates the need for manual scripting in programming languages.
Many scriptless test automation frameworks require minimal to no tester involvement in creating test scripts. Instead of writing code, testers define test scenarios and steps, which these frameworks convert into executable scripts.
The key benefit of scriptless test automation is reduced dependency on scripting expertise. It’s accessible even to non-technical users and enables enterprises to accelerate time-to-market.
Artificial Intelligence (AI) and Machine Learning (ML) have become integral across industries, including software testing.
By incorporating the two technologies into testing workflows, enterprises can automate repetitive and time-consuming tasks, freeing testers to focus on activities requiring human insight. Key applications include:
Hyper automation extends beyond traditional automation by integrating multiple technologies, such as AI, Robotic Process Automation (RPA), and process mining, to automate the entire quality assurance lifecycle.
It automates tests and optimizes workflows, environment setup, and bug triaging.
With hyper-automation, enterprises can:
Cyberattacks are increasingly threatening, causing substantial financial data and brand repudiation losses. Do you know global cybercrime costs are expected to grow by 15% per year over the next five years, reaching $10.5 trillion USD annually by 2025?
To mitigate risks, enterprises are incorporating cybersecurity testing into their pipelines. This evergreen trend focuses on identifying vulnerabilities before hackers can exploit them, protecting networks, business-critical systems, and products.
Cybersecurity testing involves penetrating systems to uncover potential weak points, ensuring robust security measures. Statista reports that 41% of enterprises have adopted a zero-trust strategy, restricting access to authorized users only.
QAOps is a new-age testing practice that’s changing the way Quality Assurance (QA) works. The QA team collaborates closely with the development and operations teams in this approach to ensure high-quality software delivery.
QA professionals play an active role in creating and managing CI/CD pipelines. They can identify and address bugs and errors early in the build process, thereby minimizing the cost of fixing them later.
Furthermore, the robust collaboration between QA, development, and IT teams helps accelerate time-to-market.
This ensures that apps perform optimally in dynamic cloud environments. This involves validating microservices, guaranteeing compatibility with container orchestration platforms like Kubernetes, and assessing system performance under varying loads.
One key benefit of cloud-native testing is its ability to integrate with cloud-based CI/CD pipelines, enabling continuous testing and faster feedback loops. It also supports scalable environments, allowing enterprises to replicate production-like scenarios during testing.
This focuses on post-production testing, emphasizing real-world performance, customer feedback, and live monitoring.
While shift-left testing concentrates on defect prevention early in the development cycle, shift-right testing ensures that software continues to meet user expectations after deployment.
This includes practices such as A/B testing, canary releases, and real-time monitoring to understand how users interact with the software. It also involves gathering insights from production environments to identify and resolve performance bottlenecks or usability issues.
Shift-right testing aligns with the DevOps philosophy of continuous improvement and helps deliver superior customer experiences.
We live in an era when data is considered the new currency, and enterprises are undoubtedly leveraging it more than ever.
Data-driven companies are 23X more likely to acquire new customers and 6X times more likely to retain them — this is from a 2014 McKinsey report, and a decade later, this is truer than ever!
Reliance on data has driven the rise of big data apps, which process and analyze massive datasets to generate actionable insights. These insights empower enterprises to make informed decisions and fuel business growth.
On the other hand, big data testing focuses on evaluating the functionality of such apps to ensure they operate efficiently, securely, and error-free while processing large volumes of data.
Blockchain testing is rapidly gaining momentum as Blockchain technology becomes more widely adopted across industries. It involves assessing various components of a Blockchain network, including smart contracts, transactions, wallets, and blocks.
Industries such as healthcare, finance, cybersecurity, and supply chain management leverage Blockchain for its features, including high-grade security, transparency, smart contracts, immutable record-keeping, and decentralization.
According to the PwC’s “Time for Trust” report, Blockchain is expected to boost the global GDP by $1.76 trillion by 2030. As Blockchain adoption increases, testing becomes crucial to ensure the technology’s reliability and performance across different use cases.
In an era where diversity and inclusion are prioritized, accessibility testing ensures that software is usable by people with disabilities.
This includes several GUI testing practices, such as evaluating compliance with standards such as WCAG (Web Content Accessibility Guidelines) and ADA (Americans with Disabilities Act).
Accessibility testing focuses on:
The future of software testing in 2025 is all about leveraging AI, automation, and advanced analytics to enhance efficiency, ensure quality, and adapt to rapidly evolving technologies and user demands.
Source: This article was originally published at testgrid.io.
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