In a nutshell: Against the backdrop of accelerating development cycles and the relentless demand for highly reliable and high-performance products, automated testing has established itself as an indispensable tool in corporate strategy. This guide covers: Intelligent automation based on AI, The next step: agent-based AI, AI supporting the Shift-Right approach to better understand the user, and More accessible “no-code” automation.
Against the backdrop of accelerating development cycles and the relentless demand for highly reliable and high-performance products, automated testing has established itself as an indispensable tool in corporate strategy.
In response to these challenges, testing services are evolving to incorporate AI-driven automation, cloud-based testing environments, and self-healing frameworks in order to ensure both greater reliability and faster delivery.
In this article, we explore the key trends in software testing that enable companies to adapt to this ever-changing ecosystem.
1. Intelligent automation based on AI
The integration of artificial intelligence (AI) and machine learning (ML) into testing processes is one of the most significant developments in recent years.
Unlike traditional static scripts, these technologies now make it possible to dynamically adapt test scenarios based on changes to the application under test.
Automated testing tools will rely on AI to:
- Automatically identify potential regressions by analyzing logs and test histories
- Generate intelligent test cases based on actual user behavior
- Optimize test coverage by targeting the highest-risk areas
The growth of AI in testing is exponential. According to a global survey of 1,775 IT and business leaders, 71% of organizations have integrated some form of artificial intelligence or generative AI capabilities into their operations, with more than one-third (34%) using it specifically to improve quality assurance.
Next step: agent-based AI
Building on multimodal AI, which processes multiple types of data simultaneously using specialized algorithms, a new generation is now emerging: agent-based AI.
These intelligent systems operate autonomously, handling tasks that were previously reserved for humans.
They are capable of:
- Communicate with each other
- Maintaining memory function over the long term
- Make independent decisions based on the context and interactions
In practice, this amounts to deploying a true virtual team of test assistants who are responsive, adaptable, and capable of ensuring continuous software quality on a large scale.
2. AI Supporting the Shift-Right Approach to Better Understand the User
By 2025, understanding actual user behavior will become a strategic pillar of automated testing approaches.
Capgemini's 2024 World Quality Report highlights that organizations are increasingly investing in advanced technologies, such as AI and machine learning, to improve their quality assurance processes and better understand user behavior.
While shift-left methodologies aim to test earlier in the development cycle, the shift-right approach complements this by focusing on post-deployment quality under real-world conditions.
By analyzing user interactions in a production environment, QA teams can identify usage patterns, anticipate potential issues, and ensure that applications meet users’ actual expectations.
What next-generation AI tools make possible:
Models based on visual captures: AI uses “snapshots” of user sessions.
Real-world behavioral analysis: Tests are automatically generated from usage data, covering both positive and negative scenarios, including rare cases.
Zero manual maintenance: AI continuously learns from interactions, eliminating the need for repetitive updates to test scripts.
Smart production testing: By combining post-deployment monitoring with traditional testing, we increase coverage and catch issues that would otherwise go unnoticed.
Improved test coverage: AI dynamically links code to real-world scenarios, making it easier to detect regressions on critical paths.
AI-assisted error analysis: automatic detection of failure causes (API, performance, business logic), saving time on the development side.
Continuous monitoring and insights: These tools provide a real-time view of usage, bridging the gap between production performance and test coverage.
3. More Accessible “No-Code” Automation
The complexity of automated testing tools has long been a barrier to their widespread adoption.
This trend is reversing thanks to the emergence of no-code and low-code solutions, which enable non-technical users (product owners, business analysts, etc.) to create and maintain test scenarios without writing a single line of code.
These intuitive tools pave the way for a true democratization of testing, offering several concrete benefits:
- Better collaboration between technical and business teams
- A significant reduction in maintenance costs and time
- Greater responsiveness to functional changes
By 2025, these solutions are expected to become the new standard for automated testing, offering unprecedented time savings and flexibility.
4. The Changing Role of Testers and the Skills Required
The roles of testers are evolving toward hybrid profiles that combine traditional skills with expertise in AI, DevOps, and automation.
This transition is already underway, as reflected in various industry data:
AI Skills: According to a McKinsey study, 62% of employees aged 35 to 44 report having a high level of expertise in AI, indicating a growing adoption of these skills.
DevOps Skills: A Brokee report highlights that 29% of IT teams have recently hired a DevOps engineer, making this the most in-demand role in the IT field.
Automation Expertise: According to PractiTest’s “State of Testing™ Report 2024, ” although automation is on the rise, many testers lack confidence in writing automation scripts, indicating a need for automation skills development.
5. DevOps/SRE Integration
The integration of automated testing into DevOps and SRE (Site Reliability Engineering) practices is emerging as one of the major trends in 2025.
As companies strive for continuous delivery and enhanced operational resilience, testing is evolving toward a proactive, continuous approach that is fully integrated into workflows.
What is Site Reliability Engineering (SRE)?
Originating at Google, Site Reliability Engineering involves applying software engineering principles to systems management and operations.
Its goal is to ensure that applications remain stable, available, and high-performing, even at scale.
In practice, SRE engineers work closely with the development teams to:
- Automate deployments
- Monitor Performance
- Anticipating Outages and Bugs
- Ensure an optimal user experience
6. Green IT Applied to Automated Testing
Environmental responsibility is becoming a strategic issue for companies across all sectors, and Green IT practices are increasingly being integrated into the field of software testing.
Test automation, while essential, can consume significant resources, particularly through the large-scale execution of test scenarios, the use of virtualized test environments, and data storage.
In 2025, increasing attention is being paid to the energy impact of automated testing.
According to a Forrester article, testing efficiency is crucial in the application development process.
Ineffective or poorly designed tests can increase energy consumption. Shifting from systematic automation to intelligent automation—using AI and machine learning to determine when to automate or run a test—can lead to significant energy savings.
Environmentally responsible testing approaches:
Optimizing test campaigns: reducing redundant runs and prioritizing high-value tests.
The use of “green” cloud infrastructure: data centers powered by renewable energy and optimized for energy efficiency.
Reducing the volume of data generated or stored unnecessarily: implementing data management policies aimed at minimizing the carbon footprint.
The Smart Use of AI: Leveraging Artificial Intelligence to Avoid Unnecessary Testing Through Targeted Recommendations.
Mr Suricate Leader in No-Code Test Automation
At Mr Suricate, we empower those who are shaping the digital future.
Since 2015, we have been turning complexity into simple, effective solutions, making quality a driver of performance and innovation.
From validation to deployment, maintain complete control over your user journeys through automated testing and real-time insight monitoring.
Further Reading
FAQ
What are the major developments in automated testing?
Intelligent automation based on AI, including agent-based AI; AI supporting the “shift-right” approach; more accessible no-code solutions; the evolving role of testers; DevOps/SRE integration; and Green IT applied to testing.
What Is Agent-Based AI in Testing?
This AI is capable of acting more autonomously during testing: proposing, adapting, and maintaining test scenarios. It assists and speeds up the process, but remains under human supervision.
Will the role of testers disappear?
No, it's evolving. Testers are moving from manual execution to strategy, automation oversight, and analysis. The focus is shifting toward quality management.
What is Green IT as it applies to testing?
It’s about reducing the testing footprint: running the right tests, avoiding wasted resources, and optimizing test runs. Quality also becomes a matter of efficiency.
How can we prepare for these changes?
By adopting a scalable no-code approach integrated with CI/CD and monitoring, Mr Suricate this transition for both business teams and key accounts.
See also: UI Testing—Critical Basics to Check.
