Intelligent approaches to software testing: automation, quality, and adaptability

Abstract

The monograph explores the key contradiction of modern software engineering: the conflict between the speed of delivery of functionality and the need to ensure the quality of complex systems. Based on the analysis of the evolution of QA practices, the systemic gaps of existing approaches to optimizing regression testing, such as impact analysis of changes (TIA) and methods based on machine learning (ML), are demonstrated. The author's methodology, Adaptive Quality and Test Impact (AQTI) Framework, is proposed as a solution. The scientific novelty of the framework is the integration of signals of the impact of changes in the code with a multifactorial assessment of the quality of the tests themselves, including their stability and business criticality. AQTI is formalized in the form of a transparent scoring model and an algorithm that allows you to create a minimally sufficient set of tests within the specified time constraints (SLA) with a guaranteed safety net of critical checks. The results of the retrospective modeling confirm that the framework is able to significantly reduce the time of test cycles while maintaining a high level of defect detection. The monograph is addressed to researchers, engineers and architects. Due to its reliance on universal engineering artifacts, the proposed approach has not only theoretical but also practical value, being applicable in industries such as fintech, e-commerce and government systems.