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The Promise of AI
At its core, the promise of AI in software testing is rooted in automation and efficiency.
The promise of Artificial Intelligence (AI) in software testing is that an intelligent agent will one day replace humans. Instead of the struggle of manual labor involved in the endless unit and integrated proofing of software quality, machines will test computer systems without human intervention. Software quality will improve dramatically, delivery times will compress from minutes to seconds, and vendors and customers will experience a software renaissance of inexpensive and user-friendly computer applications (apps).
This promise is commonly framed around:
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Fully autonomous testing
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Faster delivery cycles
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Reduced human involvement
Questions worth asking
Before accepting these claims, it is worth examining what AI can realistically deliver today.
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How can AI be used to improve software testing?
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Does the hype live up to the facts about and constraints on AI in software testing?
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What is the nature of software testing that makes autonomous means challenging to develop and implement?
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What is the true reality that AI research can deliver for the software testing industry?
These questions define the gap between expectation and reality.
The Hype
Much of the conversation around AI in software testing is driven by bold promises.
Searches on Google or any other search engine for AI in Software Testing reveal an assortment of magical solutions promised to potential buyers. Many solutions offer to reduce the manual labor involved in software testing, increase the quality, and reduce the costs to the organization. Vendors promise that their AI solutions will solve the software testing “problem”.
The hype typically emphasizes outcomes like these:
| Common promise | Intended outcome |
|---|---|
| Reduced manual labor | Less human involvement in testing |
| Higher quality | Fewer defects reaching production |
| Shorter testing cycles | Faster releases |
| Replacement of testers | Elimination of human error |
But whether this vision is desirable or even possible remains an open question.
The Reality
Software testing does not exist in isolation from human context.
The reality is far more complex and daunting when it comes to taking humans out of a human-centered process. Software development is a process for and by humans, and no matter the methodology — Waterfall, Rapid Application Development, DevOps, Agile, et al — humans remain central to the purpose of the activity.
In practice, software testing must account for:
| Human-driven factor | Why it matters |
|---|---|
| Changing business requirements | Tests must constantly adapt |
| Shifting user expectations | Quality is subjective and evolving |
| Evolving developer assumptions | Intended behavior changes over time |
These variables make fully autonomous testing extremely difficult.
Why software testing resists full automation
The roots of software testing help explain these limitations.
The initial standards and methodologies for software testing come from manufacturing product testing, where products are well-defined and testing routines are set in stone. Software testing does not allow such uniform, robotic methods of assuring quality.
In modern software development, you do not know what you do not know. AI can never anticipate or test for what it or its creators had not seen coming. Tester constraints to the imagination will also constrain AI, making true autonomy unattainable.
AI maturity in software testing
Rather than a single leap to autonomy, AI in software testing evolves in stages.
| Stage | Description |
|---|---|
| Operational | Automates repetitive execution tasks |
| Process | Supports analysis, recommendations, and optimization |
| Systemic | Attempts fully autonomous testing |
Each stage represents a different balance between automation, intelligence, and human oversight.
Operational AI
Operational AI is where most AI-enabled software testing currently resides.
At this stage, AI focuses on execution efficiency. Operational testing involves creating scripts that mimic routines human testers may have to do themselves hundreds of times. The AI here is not truly intelligent, but it reduces repetitive effort.
Operational AI typically supports:
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Shortening script creation
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Repeated test executions
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Storing and organizing results
Process AI
Process AI represents a more mature and practical use of AI in software testing.
At this level, AI moves beyond execution into analysis and recommendation. Testers can use Process AI for test generation, test coverage analysis and recommendations, defect root cause analysis, effort estimations, and test environment optimization. Process AI can also facilitate synthetic data creation based on patterns and usages.
The practical impact of Process AI can be summarized as:
| Area | Benefit |
|---|---|
| Test execution | Reduced unnecessary retesting |
| Coverage | More targeted and risk-based |
| Cost and time | Measurable efficiency gains |
Systemic AI
Systemic, or fully autonomous, AI testing remains largely aspirational.
One major limitation is the overhead required to train AI systems. Fully autonomous AI would need to test for requirements not even humans know exist. Humans would then need to verify the AI’s assumptions and conclusions, creating a new layer of complexity.
As a result, the development of autonomous software testing is asymptotic — it can be approached, but never fully realized.
Training AI
While full autonomy is unrealistic, AI that supports human testers is valuable.
Though fully autonomous AI is a chimera, developing AI that supports and extends human efforts at software quality is worthwhile. Testers must consistently monitor, correct, and train AI with evolving learning sets. Training involves assigning risks to bugs and addressing bias introduced by historical data.
The training dynamic can be summarized as:
| AI capability | Human responsibility |
|---|---|
| Pattern recognition | Validate relevance |
| Risk estimation | Confirm impact |
| Recommendations | Apply judgment |
AI can estimate probabilities, but confidence remains human-driven.
Risk Mitigation
At its core, software testing is a confidence game.
Confidence can never be 100 percent. All software testing, whether manual or AI-assisted, is risk-based. Testers decide coverage based on the likelihood and impact of failure, and AI follows the same logic.
Even when AI presents probabilities of software failure, humans must confirm and interpret the results.
Katalon Forges On with Its Vision for AI
Katalon approaches AI with a focus on practicality rather than hype.
Katalon is committed to developing and delivering AI-enabled software testing tools that are practical and effective. The goal is to reduce manual labor while producing realistic results with minimal effort required from testers.
Katalon believes the most exciting deployment of AI in software testing is at the Process AI level.
“The biggest practical usage of AI applied for software testing is at that process level, the first stage of autonomous test creation.”
Fully autonomous AI that replaces humans is hype. AI that supplements human effort and shortens test cycles is realistic, desirable, and achievable.
🖥️Watch webinar: AI adoption in test automation
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