THE AI–ANALYST–TESTER TRIANGLE: A NEW FRAMEWORK FOR INTELLIGENT SOFTWARE QUALITY MANAGEMENT

Author

Darshan Dandavathi , Prof. Swati Kanmadi


Abstract

 The rapid integration of artificial intelligence (AI) into software engineering is transforming how software requirements are interpreted, test cases are generated, defects are identified, and quality decisions are made. However, the use of AI-enabled tools does not automatically ensure software reliability, security, usability, or compliance. AI-generated outputs may contain inaccuracies, overlook contextual requirements, reproduce biases, or create misleading test results. These challenges demonstrate the need for a structured framework that combines computational intelligence with human analytical judgment and systematic software testing.

This paper proposes the AI–Analyst–Tester Triangle (AAT Triangle) as a conceptual framework for intelligent software quality management. The framework integrates three complementary components: AI as an intelligent assistant for generating insights and automating repetitive quality activities; the analyst as the interpreter of business requirements, operational context, risks, and quality objectives; and the tester as the independent evaluator responsible for validating software behaviour, detecting defects, and verifying acceptance criteria. The framework emphasizes continuous feedback, evidence-based decision-making, risk prioritization, traceability, and human accountability.

The study adopts a conceptual research design based on an analytical review of selected literature, software quality standards, AI testing guidance, and responsible AI frameworks. It develops a proposed process model, identifies potential performance indicators, formulates research hypotheses, and outlines an empirical validation strategy. The paper argues that intelligent software quality management should be understood not as the replacement of human expertise by AI, but as a coordinated process in which machine-generated intelligence, analytical interpretation, and independent verification reinforce one another. The proposed framework offers a foundation for future empirical research and practical implementation in software development organizations.



Keywords

Artificial intelligence, software quality management, software testing, business analysis, human–AI collaboration, defect detection, intelligent test automation, quality assurance, risk-based testing.



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References


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