APPROACHES TO AUTOMATING SOFTWARE QUALITY CONTROL IN DISTRIBUTED DEVELOPMENT TEAMS

Abstract

This article provides a comprehensive analysis of approaches to automating software quality control in distributed teams, where asynchronous collaboration and a high rate of change increase reliance on repeatable checks and comparable metrics. The study is conducted as a review-and-synthesis of peer-reviewed publications, extracting data on automation types, applied metrics, reported effects, and applicability conditions, without quantitative aggregation due to methodological heterogeneity. Particular attention is paid to how automation is distributed across stages of the build-and-delivery pipeline, as well as to the role of intelligent methods in accelerating feedback and reducing redundancy in regression checks. The findings show that research and practice are primarily concentrated in testing loops, as they provide a regular stream of quality measurements, while other stages are represented much more weakly. It is established that the greatest practical effect is achieved by moving from the “accumulation of tools and metrics” to a single managed control loop that combines automated checks, process metrics, and formalized rules for interpreting and applying them. The paper demonstrates that an evidence-based approach linking “problem–process–metric” improves decision reproducibility and action comparability across teams, and that semantic and hybrid decision-support methods outperform keyword-based search in complex distributed contexts. The results may inform the design of managed quality automation frameworks for distributed software development.