‘Every company is a software company’ –

noted the McKinsey Quarterly dated Dec 2022 and discussed how businesses, both traditional and new-age, are becoming ‘tech-enabled’ and shifting to software-driven business models.

Three distinct software-driven business models are observed by McKinsey team :

1. Traditional businesses are reorienting their operations around software and investing heavily in software to enhance their operational efficiency, customer experience, while many more companies are delivering value to consumers through software embedded in their products & services.
2. Companies in certain industries like manufacturing and banking & finance are building software business verticals to complement their existing product range – a new revenue stream is emerging.
3. Some legacy and large businesses are converting their internal software solutions into marketable products, and thus exploring new revenue opportunities

With every sector embracing digital transformation, the role of software testing and quality assurance (QA) is becoming increasingly significant. There is a rising demand for high-quality software, which is driving up the need for efficient software testing solutions and processes.

The 7 Major Trends Transforming Software Testing and QA in 2025

1. The Dynamic penetration of AI

Generative AI is Transforming Software Engineering & Software testing with Diverse Tools. Simform’s 2024 Generative AI survey confirms the excitement around empoying AI – “an impressive 61% of organizations prefer using Gen AI for tasks like Code generation and auto-completion. While 29.3% are using GenAI for Code refactoring and optimization” (source for this data point)

AI-powered testing is bringing about higher levels of efficiency, accuracy and scalability to the software development lifecycle. Leveraging machine learning algorithms, AI can identify patterns, predict potential issues, and generate smarter test cases, significantly reducing the time required for manual testing. AI-driven tools are being employed to:

• automate Test case generation by analysing historical data and application logs
• enable faster test creation for complex workflows while reducing the manual effort of testers
• predict potential system failures using predictive analytics
• optimize testing processes through intelligent scheduling and prioritization
• enable self-healing test scripts which adapt to changes in the application, thus minimizing maintenance investments
• optimize test coverage by analyzing large datasets to highlight high-risk areas and address critical issues early in the cycle

Muffins using the power of AI

Muffins, an AI-powered No code test automation platform, addresses the limitations of traditional tools like Selenium and Cypress. These tools rely heavily on static locators, making them prone to frequent maintenance as applications evolve.

Muffins leverages advanced machine learning to create adaptive, self-healing test suites. These models analyze DOM structures etc, while dynamically updating test scripts to account for structural and functional shifts in the application under test (AUT).

Key AI-driven features are :

1. Intelligent Test Step Generation: AI analyzes test cases and generates optimized, well-structured test steps that are aligned with business logic, edge cases, and specific requirements. This results in more actionable and effective testing.
2. Self-Healing Capabilities: Muffins employs intelligent algorithms to automatically adapt test scripts to rapid application changes, significantly reducing maintenance costs in agile environments.
   
   

Our teams are exploring how to incorporate AI into the test automation process and leverage its capabilities to our advantage.

2. Shift-Left and Shift-Right Testing

The shift-left and shift-right testing approaches are becoming mainstream in the  software development lifecycle.

1. Shift-Left Testing  focusses on integrating testing early in the development process, enabling teams to identify and address defects before they escalate and turn into expensive issues. This proactive approach often incorporates practices like automated unit testing, continuous integration, behavior-driven development (BDD) and test-driven development (TDD).
   
   Ensuring API security with a Shift-left approach involves embedding security measures into the design, coding and testing phase. Through this potential issues in APIs can be identified and mitigated before deployment. By addressing security concerns earlier, organizations can reduce risks, enhance the robustness of their APIs, and save time and resources that might otherwise be spent on addressing issues post-deployment.
2. Shift-Right Testing  extends quality assurance into production, emphasizing observability, performance monitoring, real world feedback and user experience. This approach employs chaos engineering and A/B testing.

Together, shift-left and shift-right testing create a comprehensive, end-to-end framework that ensures robust quality assurance process, reduced TTM and improved user experience.

3. Scaling Testing with Cloud infrastructure

As cloud adoption increases, scaling resting with cloud infrastructure is gaining momentum. This transformative trends enables companies to execute large-scale, efficient and flexible testing processes. Cloud testing results in:

• scalability to run extensive test suites in parallel across diverse environments and configurations
• integration with CI/CD pipelines, continuous testing and faster feedback loops
• reduced infrastructure costs and maintenance efforts
• remote collaboration and accessibility for globally distributed teams

As businesses embrace agile and DevOps methodologies, leveraging cloud infrastructure for testing has become essential to meet the demands of rapid development cycles and deliver high-quality software.

4. Performance Engineering over Performance Testing

There has been a growing emphasis on building performance optimization into the entire software development lifecycle. Performance engineering takes a proactive approach by designing systems for optimal performance from the beginning, instead of merely testing them after development. This would :

• facilitate stronger collaborations between QA teams, developers, architects and operations in order to identify potential bottlenecks, optimize code and to integrate performance considerations early in the SDLC
• enable QA teams to adopt chaos engineering practices and ensure resilience under adverse conditions
• have teams to utilize observability tools that monitor performance metrics real-time

5. Agentic AI powering Human & AI collaboration

Agentic AI refers to autonomous “agents” that make decisions, plan actions, and solve problems independently, with minimal human intervention. Recent agentic AI launches include Salesforce Agentforce, Google Cloud’s Agentspace, Amazon Bedrock from Amazon Web services. Gartner predicts that by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024, enabling 15% of day-to-day work decisions to be made autonomously.

We are quoting ChatGPT here : Agentic AI, with its advanced capabilities, can offer significant support to testers, no-code test automation platforms, and QA processes.

For Testers

1. Smart Test Case Generation: Agentic AI can analyze application requirements, logs, and user behavior to generate optimized and comprehensive test cases.
2. Automated Defect Detection: Using AI-driven analytics, it identifies patterns in logs and systems to detect potential issues before manual testing begins.
3. Real-time Support: Acts as an intelligent assistant, providing testers with suggestions for edge cases and test improvements.
4. Test Data Management: Simplifies the creation of synthetic test data by understanding system requirements and generating diverse datasets.

For No-Code Test Automation Platforms

1. AI-Enhanced Automation: Agentic AI can enhance no-code platforms by automatically mapping user flows and converting them into reusable test scripts.
2. Visual Recognition: Offers improved visual-based element recognition for platforms using drag-and-drop interfaces, reducing false positives in UI testing.
3. Natural Language Inputs: Allows testers to describe test scenarios in plain language, which the AI converts into executable automation scripts.
4. Adaptive Testing: Enables dynamic script adaptation when application changes are detected, minimizing maintenance overhead.

6. Cybersecurity Testing, the non-negotiable

Since Cybersecurity threats are more prevalent than ever, Cybersecurity testing will become a non-negotiable part of QA. Big waves in security testing would be :

• Integration of security testing tools into CI/CD pipelines for continuous vulnerability assessments
• Use of ethical hacking techniques and penetration testing to uncover weaknesses
• Penetration testing automation – AI driven tools will simulate complex attack scenarios to identify & strengthen vulnerable spaces
• Emphasis on API security testing
• Compliance testing – QA teams need to ensure that software adheres to global data privacy standards like GDPR, CCPA etc

7. Exploratory and Context drive Testing + User experience Testing

Both Exploratory & Context drive testing and User experience testing, ensure the software’s real-world usability and engagement. While automation is a dominating theme, the significance of exploratory testing will stay on; and UX too, which happens to be the critical differentiator. QA engineers in 2025 will focus on :

• Adapting test strategies based on application context and user personas
• Leveraging exploratory testing to uncover edge cases and usability issues
• Using advanced tools to document and share exploratory testing insights effectively

UX testing focuses on evaluating the application’s usability, accessibility, and overall experience from the end-user’s perspective. This aligns with the growing emphasis on customer satisfaction, as more and more businesses recognize the importance of delivering seamless, intuitive, and enjoyable digital experiences.

• Usability testing to ensure applications are intuitive and user-friendly
• A/B testing to evaluate different design choices and their impact on user behavior
• Accessibility testing to comply with standards like WCAG and ensure inclusivity

To conclude,

Innovations anchored in AI, Cloud computing, etc and agility & user-centricity will dominate Software testing and QA in 2025. As software becomes more complex, Quality Assurance would evolve from traditional testing to multi-dimensional testing covering cybersecurity, ethical AI etc. These trends are essential for businesses which are striving to deliver distinct experiential value through highly reliable software and to retain their edge in today’s fiercely competitive environment.