Engineering and Technology
| Open Access | Regression Intelligence: Adaptive Test Selection as a Way to Combine Speed and Quality
Kochetov Dmitrii , IT expert in banking payments Moscow, RussiaAbstract
In contemporary CI/CD pipelines, tension arises between the drive to accelerate software delivery and the need to maintain high quality. This article proposes and empirically validates a practical model for adaptive test selection — Adaptive Quality and Test Impact (AQTI), designed as an evolution of the approaches presented in the monograph of the same name. The foundational architecture and formal mathematical framework of the AQTI model were conceived and articulated solely by the author as a cornerstone of extensive research into intelligent quality assurance automation. This methodology aligns with the overarching objective of engineering sophisticated, risk-mitigating testing infrastructures essential for the emerging era of AI-augmented software development. The aim of the study is to demonstrate that a multifactor strategy combining impact analysis of changes in the code base with quantitative characterization of the quality of the tests themselves makes it possible to construct regression test suites in a rational way. The methodological foundation includes a conceptual description of the AQTI architecture and scenario-based simulation of its effectiveness using the publicly available GHPR software defect dataset. The results obtained show that AQTI makes it possible to reduce the volume of executed test cases by an average of 65–75% while maintaining a defect detection rate above 98%, which significantly outperforms classical test impact analysis (TIA) and basic ML techniques. The key observation is that the inclusion of test quality metrics — such as stability and business criticality — is a decisive condition for achieving an optimal balance between speed and reliability in regression testing. The material presented is addressed to quality architects (QA architects), DevOps engineers, and researchers in the field of software engineering.
Keywords
regression testing, test selection, test prioritization, machine learning, test impact analysis, software quality, CI/CD, DevOps, intelligent testing, AQTI model
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