DeviQA Standardizes Its Software Testing Methodology for AI-Assisted Software Development

DeviQA formalizes a QA methodology for AI-assisted development, addressing new risks in AI-generated code, tests, and regression.

DeviQA, a software quality engineering company with 16+ years of experience, has formalized its testing methodology for software developed or modified with AI coding tools such as GitHub Copilot, Claude Code, Cursor, and similar platforms.

AI has made coding faster, but confidence still requires verification. The faster software changes, the more important it is to focus testing on the highest-risk areas.”

— Oleg Sadikov, co-founder and CEO at DeviQA

The methodology brings together practices DeviQA has already been applying across AI-assisted development projects and standardizes how teams assess the additional risks introduced by AI-generated code.

AI coding tools are changing the speed and scale of software development. DeviQA’s 2026 research found that 65% of development teams actively use AI coding tools, while 52% reported an increase in bug volume and 58% saw higher testing workloads. In addition, 74% of QA professionals said they change their quality engineering approach when they know code was AI-generated.

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“We didn’t suddenly start testing AI-generated code differently,” said Oleg Sadikov, CEO of DeviQA. “Our teams have been adapting their QA approach as AI became part of everyday development. What we’re doing now is turning those proven practices into a consistent, replicable framework. The industry is moving code generation faster than verification can keep pace, and we’ve learned how to close that gap.”

What changes when AI is involved in development

Traditional QA practices remain relevant, but AI-assisted development introduces additional failure modes. Generated code can be syntactically correct while carrying incorrect assumptions, incomplete business logic, excessive implementation scope, or hidden dependencies across the system.

DeviQA’s methodology therefore strengthens several areas of verification:

– Independent verification. AI-generated tests are not treated as sufficient proof that AI-generated implementations are correct. When the same assumptions influence both implementation and validation, defects can pass through both layers.
– Behavioral impact analysis. Regression scope is determined by the potential impact of a change across workflows, integrations, permissions, data, and dependencies — not simply by the number of lines changed.
– Adversarial testing. Teams place greater emphasis on edge cases, unexpected inputs, integration failures, inconsistent data, permission boundaries, concurrency, and recovery scenarios.
– Validation of generated tests. AI-created tests are assessed for business relevance, meaningful assertions, failure-path coverage, and whether they actually validate intended system behavior.
– Continuous risk-based verification. Risk assessment and regression testing are brought closer to development so verification can keep pace with shorter AI-assisted development cycles.

The methodology reflects a growing imbalance in modern software engineering: the cost of producing code is falling faster than the cost of proving that the code works correctly.

The standardized methodology is now applied across DeviQA engagements where AI-assisted development forms part of the engineering workflow.

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