The future of automated testing: how will it evolve in 2025 and beyond?

In a context of accelerating development cycles and incessant demand for highly reliable, high-performance products, automated testing has become an indispensable tool in corporate strategy.

Faced with these challenges, test services are evolving to incorporate AI-driven automation, cloud-based test environments, and self-repairing frameworks to ensure both greater reliability and faster delivery.

In this article, we explore the main 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 represents one of the most significant developments of recent years.

Far from traditional static scripts, these technologies now enable test scenarios to be dynamically adapted to changes in the application under test.

Automated testing tools will rely on AI to :

• Automatically identify likely regressions by analyzing logs and test histories
• Generate intelligent test cases based on actual user behavior
• Optimize test coverage by targeting high-risk areas
  

The rise of AI applied to 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 over a third (34%) using it specifically to improve quality assurance.

Next step: agentic AI

Following on from multimodal AI, which processes several types of data simultaneously using specialized algorithms, a new generation is now emerging: agentic AI.

These intelligent systems operate autonomously, taking on tasks previously reserved for humans.

They are able to :

• Communicate with each other
• Maintaining long-term memory
• Make independent decisions based on context and interactions
  

In practice, this means deploying a virtual team of test assistants who are responsive, adaptive and capable of ensuring continuous software quality on a large scale.

2. AI at the service of Shift-Right to better understand the user

In 2025, understanding real user behavior is becoming a strategic pillar of automated testing approaches.

Capgemini's World Quality Report 2024 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.

Whereas shift-left methodologies aim to test earlier in the development cycle, the shift-right approach complements them, by focusing on post-deployment quality, under real-life conditions.

By analyzing interactions in production environments, QA teams can identify usage patterns, anticipate potential problems and ensure that applications meet users' actual expectations.

What next-generation AI tools can do: 

Models based on visual captures: AI uses snapshots of user sessions.

Real-life behavioral analysis: tests are generated automatically from usage data, covering positive and negative scenarios, including rare cases.

Zero manual maintenance: AI learns continuously from interactions, eliminating the need for repetitive updates of test scripts.

Intelligent production testing: by combining post-deployment monitoring with traditional testing, you can increase coverage and catch undetected problems.

Improved test coverage: AI dynamically links code to real cases, making it easier to detect regressions on critical paths.

AI-assisted error analysis: automatic detection of causes of failure(API, performance, business logic), saving development time.

Continuous monitoring and insights: the tools provide a real-time view of usage, bridging the gap between production performance and test coverage.

3. Making "no-code" automation more accessible

The complexity of automated testing tools has long been a barrier to their widespread adoption.

This trend is being reversed by the emergence of no-code and low-code solutions, which enable non-technical profiles (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, with several concrete advantages:

• Improved collaboration between technical and business teams
• Significant reduction in maintenance time and costs
• Greater responsiveness to functional changes
  

By 2025, these solutions are set 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 towards hybrid profiles, combining 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 say they have high AI expertise, indicating growing adoption of these skills.

DevOps knowledge: a Brokee report points out that 29% of IT teams have recently recruited a DevOps engineer, making this the most sought-after position in IT.

Automation expertise: according to PractiTest's "State of Testing™ Report 2024", although automation is growing, a large number of testers lack confidence in writing automation scripts, indicating a need for automation skills development. 

5. DevOps / SRE integration

The integration of automated testing with DevOps and SRE (Site Reliability Engineering) practices is set to be one of the major trends in 2025.

As companies aim for continuous delivery and enhanced operational resilience, testing is evolving towards a proactive and continuous approach that is seamlessly integrated into workflows.

What is Site Reliability Engineering (SRE)?

Born at Google, Site Reliability Engineering involves applying software engineering principles to systems management and operations.

Its aim is to ensure that applications remain stable, available and efficient, even on a large scale.

In concrete terms, SRE engineers work closely with development teams to :

• Automate deployment
• Monitor performance
• Anticipate breakdowns and bugs
• Ensuring an optimal user experience
  

6. Green IT applied to automated testing

Environmental responsibility is becoming a strategic issue for companies in all sectors, and Green IT practices are becoming increasingly integrated into software testing.

Test automation, while indispensable, can consume significant resources, notably through massive scenario execution, the use of virtualized test environments and data storage.

In 2025, increasing attention will be paid to the energy impact of automated testing.

According to an article by Forrester, effective testing is crucial to the application development process.

Inefficient or poorly designed tests can increase energy consumption. Moving from systematic to intelligent automation, using AI and machine learning to determine when to automate or run a test, can deliver significant energy savings.

Eco-responsible testing approaches :

Optimize test campaigns: reduce redundant executions and prioritize high value-added tests.

The use of "green" cloud infrastructures: data centers powered by renewable energies and optimized for energy efficiency.

Reduce the volume of data generated or stored unnecessarily: implement data management policies to minimize carbon footprint.

Intelligent use of AI: harnessing artificial intelligence to avoid unnecessary tests through targeted recommendations.

Mr Suricate - Leader in no-code test automation

At Mr Suricate, we put the power back in the hands of those who are building tomorrow's digital world.

Since 2015, we've been transforming complexity into simple, effective solutions, making quality a driver of performance and innovation.

From validation to deployment, maintain complete control over your user paths thanks to automated testing and real-time tracking of your insights.