Optimizing operational costs for ensuring the quality of digital products through predictive risk scoring

Анотація

The article considers the problem of optimizing operating costs when ensuring the quality of digital products using predictive risk assessment in modern software development environments. The study’s relevance lies in the need to balance high product quality and cost-effectiveness amid accelerating release cycles and increasing system complexity. The aim of the article is to examine methodological approaches to the adaptive distribution of testing resources based on predictive risk assessment to increase the cost-effectiveness of quality assurance processes. To address the tasks set, the following methods were used: analysis of the scientific literature to examine current developments in the field; generalization and systematization to present the research results.

The study examined the economic nature of risk-based testing and its role in reducing inefficient resource allocation compared to traditional universal approaches. The applicability of predictive models, particularly the Quality Risk Score (QRS), as an integrated indicator that combines code complexity, defect density, change variability, test coverage, and interaction instability to identify high-risk components was analyzed. It was found that using normalized and weighted indicators enables the formation of a consistent, scalable structure for assessing quality risks in dynamic CI/CD environments. The feasibility of implementing adaptive testing strategies that allow varying the intensity and scope of test activities based on calculated risk levels was substantiated. The study also analyzed the impact of this approach on key performance indicators, in particular the volume of test work, time consumption, and defect detection efficiency, demonstrating a significant reduction in overall costs while maintaining a high level of quality control. It has been found that adaptive redistribution of testing resources increases the efficiency ratio, reflecting a higher return on each unit of invested resources.

As a result, it was concluded that predictive risk assessment is an effective tool for achieving cost optimization while maintaining the reliability and competitiveness of digital products in rapidly changing technological environments.