Revolusi Ilmiah dalam Pengembangan Machine Learning untuk Penjaminan Kualitas Perangkat Lunak - Nyoman Ayu Gita Gayatri (Indonesian Character Journal, 2026)
The integration of Machine Learning (ML) into Software Quality Assurance (SQA) is commonly discussed as a technical advancement in defect prediction, test automation, and software reliability. Such a perspective, however, is insufficient for understanding the human consequences of this transformation. This study repositions ML-driven SQA as a scientific, ethical, and character-development issue in the transition towards Industry 5.0. Drawing on Thomas S. Kuhn’s theory of scientific revolutions, Abraham Maslow’s psychology of science, and the principles of Trustworthy AI, this article examines how the transition from deterministic testing to learning-based quality assurance changes the professional identity and moral responsibilities of software engineers. The study employs a qualitative interdisciplinary design through a Systematic Literature Review, Conceptual Framework Analysis, and philosophical hermeneutics. The synthesis indicates that conventional SQA has entered a Kuhnian crisis because rule-based testing is increasingly unable to accommodate the uncertainty, scale, and dynamic interdependence of cloud-native and AI-enabled systems. ML introduces a new, probabilistic paradigm that is incommensurable with the former paradigm in terms of evidence, decision-making, and the role of engineers. The article argues that this transition requires engineers to move beyond a safety-oriented mentality characterized by dependence on certainty and procedural control toward a growth-oriented character marked by epistemic courage, adaptability, humility, ethical responsibility, and human–AI collaboration. The study proposes the Kuhn–Maslow–Trustworthy AI Framework as a conceptual foundation for defining the character of engineers in Industry 5.0. The findings emphasize that trustworthy and explainable AI should not be treated as optional technical features, but as moral infrastructures for responsible software quality decisions.