5 Main Software Testing Trends for 2025
Opinion article by Giridhar Rajkumar. 1. Integration of AI and Machine Learning The role of AI and machine learning in the software testing industry continues to grow every year and is predicted to become increasingly influential. AI will transform various software testing activities, including generating new test cases, enabling self-healing capabilities and creating test data to reduce manual effort. It improves automated testing by creating code snippets, thus allowing testers to focus on their core tasks. In addition, AI supports testers by prioritizing critical tests, detecting anomalies and identifying the root causes of system failures, or of the tests themselves. This includes categorizing failures into product defects, automation defects or faults. 2. Shift-Left and Shift-Right Testing In today's fast-paced software development lifecycle, it is essential to get feedback on tests quickly and efficiently. Traditional software testing methods can delay the development lifecycle by providing information in later phases: Shift-Left testing is an approach to getting feedback faster to help programmers fix problems/defects as quickly as possible, helping to reduce the cost and time associated with fixing defects. On the other hand, they can also be improved by Shift-Right testing, which extends testing into production, using techniques such as A/B testing, canary releases and blue/green deployments to gather feedback from users. Active monitoring systems gather information on performance and identify faults to ensure that the software meets real-world requirements. Once a feature has been released, end-to-end testing, which includes user interface testing, can also successfully validate it. 3. ethical AI testing AI is playing an increasingly important role in software testing, but ethical practices should guide its use. As AI evolves, it can unintentionally generate biased results, leading to unfair or discriminatory outcomes. And that's where ethical AI comes in. Ethical AI testing ensures that systems meet key standards such as fairness, accountability and compliance with regulations such as the GDPR (General Data Protection Regulation), which protects sensitive data. This means continually testing the results produced by AI systems to maintain security, robustness and reliability. 4. Increasing Demand for Low-Code Testing Platforms The demand for low-code testing platforms continues to grow as many organizations prefer faster and more efficient ways of delivering high-quality software. These platforms allow non-technical stakeholders, such as commercial and UAT (user acceptance testing) testers, to create, execute and maintain automated tests with minimal coding experience. By bridging the skills gap, low-code platforms promote seamless collaboration between the company, developers and testers. Simple drag-and-drop functionality speeds up test creation, reducing test development time. With CI/CD support, low-code platforms allow tests to be run efficiently from pipelines, providing quick feedback. They help simplify Agile and DevOps practices, increasing their accuracy. In addition, low-code tools increase scalability, simplify maintenance and increase productivity by automating repetitive tasks, making software testing more reliable. 5. cybersecurity-focused testing By 2025, cybersecurity testing will increase among organizations as the frequency of cyberattacks increases. Many reputable organizations are increasingly vulnerable to threats such as phishing, data breaches, distributed denial of service (DDoS) attacks and ransomware, which can lead to financial losses and operational disruptions. Companies therefore need to consider implementing cybersecurity into their development lifecycles. One such approach is DevSecOps, in which security is applied at every stage of development activities. Proactive practices such as penetration testing, static application security testing (SAST), dynamic application security testing (DAST) and threat modeling can help organizations identify vulnerabilities early and mitigate risks proactively. AI-based tools can help with real-time monitoring, faster attack detection and predictive analysis to stay ahead of evolving threats. The continuation of the original article via Get XRay can be read here.
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