The Economic Efficiency of Software Testing Automation for Cost Optimization and Profitability Enhancement of IT Projects

Annotation. The article examines the economic efficiency of software testing automation as a strategic factor in reducing project costs and strengthening profitability in the IT sector. Unlike traditional manual approaches, automated testing is presented not only as a cost-cutting mechanism but also as a source of methodological innovation that transforms quality assurance into a proactive component of project management. The study emphasizes the integration of advanced frameworks – such as continuous testing environments, DevOps-oriented pipelines, and model-based testing – that enable organizations to detect defects earlier, shorten release cycles, and ensure higher product stability. By embedding automation directly into agile and DevOps workflows, firms can accelerate delivery without compromising quality standards. Particular attention is devoted to intelligent orchestration tools and AI-assisted techniques for data-driven test case generation, which reduce human bias and improve coverage of complex user scenarios. Hybrid methods that combine regression automation with exploratory testing are interpreted as essential for balancing efficiency with creative error detection. These practices are shown to optimize resource allocation, minimize the risks of critical failures, and provide a measurable return on investment by linking testing outcomes to financial indicators of project success. The article also presents empirical evidence from IT projects of various scales, demonstrating how automation frameworks enhance scalability, adaptability to volatile market conditions, and transparency in quality metrics for stakeholders. Overall, testing automation is conceptualized not merely as a technical upgrade but as an economic and strategic instrument that strengthens competitiveness. It allows companies to align budget efficiency with long-term innovation capacity, ensuring both immediate savings and sustainable profitability in dynamic IT markets.