Moving from 2024 and into 2025, the global software testing industry remains healthy, with an expected growth rate of 7% CAGR through 2032. There is an emphasis on AI (artificial intelligence) to improve automated test development and provide analytics to improve efficiency in 2025. Continually developing AI and ML (machine learning) tools remain hot topics.
The IoT market will significantly impact testing as well. As global availability of websites and mobile apps continues, supporting an array of devices such as smart TVs and other smart products, IoT testing becomes a necessity.
Testing roles are evolving. QA testing roles continue to move towards overall quality engineering (i.e. QAOps) in addition to identifying defects alone. A greater emphasis on data testing, data security, performance testing, and a continued focus on security will keep testers busy. Here are the top five QA testing trends shaping QA testing to improve quality for customers in 2025.
#1 - AI/ML Test Automation & Analytics
Since application providers want to maximize output without affecting quality, automated test development continues to be a focus for testing. Companies seek ways to utilize testing resources and improve test quality, create highly efficient test teams, and support the speed of Agile development methodologies.
AI and ML-powered automated testing tools are maximizing productivity by:
- Using predictive analytics to focus test development and execution in the areas of the code affected by changes
- Providing self-healing technology to improve the tester’s ability to maintain automated test scripts
- Increasing testing efficiency with data-driven analytics that expose needed improvements in the testing process
- Supporting test development’s changing needs at the speed of Agile
Many tools provide more support for testers to script tests and for determining what tests to create and when to execute them. Testers improve efficiency by reducing repetitive tasks and duplicate work all while improving defect identification early in the development process.
#2 - Test Data Management & Data Security Compliance
The expanding reliance on AI/ML technology tools and analytics means testing teams must include data testing. Data testing helps ensure that the data used to make business decisions, prioritize, and direct testing efforts is accurate. Only accurate data can provide reliable analytics. Testing teams need to ensure data remains accurate. Expect to add tests to verify data and data system performance for improved user experience.
Managing test data becomes more complicated as data security regulations expand. Test teams need to generate accurate test data and manage it to comply with data privacy regulations. Testers may also be responsible for ensuring compliance to various government bodies. Most testing teams will move to using synthetic test data (STD) generated by testing tools, which recreates actual data without compromising security or privacy.
#3 - Quality Engineering vs Testing
As AI tools impact how testing is performed, the software tester’s role may shift to increased focus on quality engineering throughout development and after release. In this scenario the QA tester role moves to auditing processes, data, and supporting the organization’s quality initiatives — testers become product quality auditors to evaluate processes, tools, and procedures.
Quality engineering may evolve to include user-centric testing where performance, usability, and digital accessibility are verified. User experience continues to play a vital part in ensuring customer satisfaction.
#4 - IoT Testing
IoT (Internet of Things) continues to impact QA testing teams as system connectivity improves. Testing IoT includes testing device functionality, security, and compliance with one or more integrated applications. The importance of IoT testing will increase as consumers demand higher-performing connectivity between IoT devices, applications, and data systems.
Connectivity security remains one of the most vulnerable pieces of an integrated system — QA testers must level up their skills in device testing and security. Extensive testing will be required to verify secure connectivity and compliance with data security regulations. IoT testing complexity ranges from the simple to the extremely complex to cover all the permutations of physical location, indoor/ outdoor environments, user behavior, activity the device is used for, security considerations, and so much more. As more devices are integrated into application systems, IoT testing will become an increasing priority for tech teams.
#5 - Cybersecurity
In 2025, expect more pressure to integrate security into code throughout the development cycle. Security testing becomes an everyday occurrence rather than a once-per-release cycle by an external team. Organizations may continue to supplement security testing with vulnerability scans and penetration testing of a released product, but the focus will be on implementing full security testing within each Agile iteration.
QA testers will start performing application penetration testing throughout the development cycle — security testing responsibility therefore moves onto the development team for effective management. Security hardening requires constant change, as teams must keep up with new vulnerabilities to ensure products are safe from bad actors.
For testing teams using AI testing tools, continuous testing for security is essential. Security testing at the development level will complement ongoing monitoring efforts to control security incidents.
Software testing continues to have a bright future but with significant changes to traditional testing processes. Agile, DevOps, and QAOps teams need testing within a tight development cycle. AI/ML test automation tools help satisfy the need for efficient and effective test automation that improves quality without straining resources.
In 2025, a QA tester’s role continues to evolve towards an auditor responsible for engineering in and supporting application quality. Testers may not be writing or executing tests as much as in the past, but instead focusing on reviewing tests for validity and compliance with ethical, quality, and security standards. Data and IoT testing will impact the testing team’s schedules and force inclusion into regular testing processes.
