Wednesday Jan 28, 2026
Wednesday Jan 28, 2026
Wednesday, 28 January 2026 12:01 - - {{hitsCtrl.values.hits}}
In the next few years, software testing — a critical but traditionally manual phase of development — is poised for a remarkable transformation. According to a recent report from IDC (International Data Corporation), by **2028, generative AI-based (GenAI) tools will be capable of writing up to 70% of software tests, dramatically reducing the need for human involvement in a task that once consumed significant time and resources.
This projection reflects not only the rapid advances in AI technology but also the shifting priorities of businesses seeking faster software delivery, improved quality, and greater efficiency. As AI tools become more intelligent and integrated into development workflows, companies are rethinking how software quality assurance (QA) is done — and what role human engineers will play in the future.
Why Testing Matters — and Why It’s a Bottleneck
Software testing is essential for ensuring applications work correctly and securely. Before software reaches end users, it must be tested for defects, performance issues, usability problems, and security vulnerabilities. Traditionally, this process has involved writing test cases manually, running them repeatedly, and analyzing results — all of which are time-consuming and expensive.
In complex systems, test coverage must be thorough. Missed bugs can damage reputations, lead to costly customer support issues, and even create legal or safety problems. Yet despite its importance, testing has often been treated as a bottleneck — slowing release cycles and consuming budgets without being directly tied to new features or revenue.
The GenAI Revolution in Testing
Enter GenAI — the subset of artificial intelligence that focuses on generating content, from text to code. Recent advances in Large Language Models and machine learning have enabled tools that can write code, analyze patterns, and make decisions based on learned examples. Applied to software testing, these technologies can automatically generate test scripts, prioritize test cases, and even suggest why a test might have failed.
According to IDC’s FutureScape: Worldwide Developer and DevOps 2024 Predictions — Asia/Pacific (Excluding Japan) report, GenAI tools will be capable of creating 70% of software tests by 2028, significantly cutting down the manual effort typically associated with quality assurance.
This level of automation could transform development pipelines. Instead of QA teams writing test scripts line by line, AI systems could generate tests based on code changes, user stories, or even natural language descriptions of desired behavior. Developers and quality engineers would then review and refine these tests rather than building them from scratch.
Benefits of AI-Driven Testing
The impact of GenAI on software testing goes beyond just writing code:
1. Faster Releases
With AI generating tests automatically, development teams can shift from weeks of manual testing to near-instant test creation. This accelerates release cycles and supports continuous integration and delivery practices.
2. Improved Test Coverage
AI tools can analyze complex systems and identify scenarios that human testers might miss. They can create tests for edge cases and unusual input combinations that improve overall coverage and reduce risk.
3. Better Usability and Quality
Automated testing can focus not only on correctness but also on user experience elements. By generating tests tailored to real-world interactions, AI can help ensure software behaves as users expect.
4. Higher Productivity for QA Teams
Rather than spending time on repetitive tasks, QA professionals can focus on strategy, exploratory testing, and ensuring that critical aspects of software — like security and compliance — are handled with human attention.
What Organizations Are Prioritizing
Research indicates that many enterprises — especially in the Asia-Pacific region excluding Japan (APEJ) — see testing and code review as key areas where AI can help developers streamline their work. IDC’s surveys found that 48% of enterprises in this region view AI as particularly useful for these tasks.
AI enhancements in testing emphasize several specific areas:
These capabilities can reduce manual maintenance and help large codebases remain robust even as features evolve rapidly.
Challenges and Considerations
While the 70% figure is impressive, it does not mean human testers will disappear. Instead, the nature of QA work will shift. Humans will focus more on strategic decision-making, understanding complex requirements, and handling areas where context, judgment, and creativity are essential.
There are also practical challenges. AI systems must be trained, monitored, and evaluated carefully to ensure the tests they generate are accurate and meaningful. Organizations that over-rely on automated results without adequate oversight risk introducing false confidence in software quality.
Security and ethical concerns also matter. Knowing that AI can generate tests is one thing — ensuring those tests do not inadvertently introduce bias or miss critical vulnerabilities is another.
Shaping the Future of Software Development
The prediction that GenAI will write 70% of software tests by 2028 highlights a broader trend: AI is rapidly becoming an integral part of software engineering workflows. Development, testing, and deployment are all evolving as tools become smarter and developers adopt new ways of working.
For businesses, this means opportunities to innovate faster, reduce costs, and improve product quality. For software professionals, it means adapting to new roles where collaboration with AI tools becomes a daily norm.
The future of testing will be less about repetitive tasks and more about intelligent oversight — ensuring that as machines generate code and tests, humans guide, verify, and refine the outcomes to build safer, more reliable software.
