Methods for Automated Detection and Localization of Defects in Program Code Through Continuous Testing

Abstract

The article is dedicated to the study of methods for automated detection and localization of defects in program code within continuous integration environments. The relevance of the research is determined by the growing complexity of distributed software architectures and the increasing dependence of development processes on continuous testing pipelines. The novelty of the work lies in the integrated analytical examination of detection stability, regression test selection, prioritization strategies, spectrum-based localization, trace reconstruction, and slicing refinement within a unified CI diagnostic loop. The work describes mechanisms for writing automated tests that verify login flows, payment operations, interface elements, forms, and APIs, as well as approaches to executing these tests on every code change to ensure early defect exposure and regression protection. Special attention is paid to the interaction between algorithmic ranking models and developer feedback in practical debugging scenarios. The study sets itself the goal of systematizing engineering mechanisms that enhance diagnostic precision under time constraints. Comparative analysis and source synthesis methods are used. The conclusion substantiates that adaptive coordination of testing layers increases reliability. The article will be useful for software engineers, QA automation specialists, and CI architects.