<![CDATA[Automated Code Review Tools (ACRT) have become increasingly integral to modern software development workflows, particularly within continuous integration and deployment (CI/CD) environments. This study aims to evaluate the effectiveness of ACRT in improving software quality, accelerating vulnerability remediation, and enhancing developer productivity. Using a combination of empirical analysis, industry case studies, and academic benchmarks, we examine how tools such as SonarQube, CodeQL, Copilot Autofix, and secret scanners impact key quality metrics including defect density, Mean Time to Repair (MTTR), and pull request (PR) throughput. A quasi experimental design was employed using Interrupted Time Series (ITS) and Regression Discontinuity Design (RDD) to measure longitudinal outcomes across six open source and enterprise projects. Results indicate that defect density decreased by 15–30% following ACRT adoption, accompanied by notable improvements in security MTTR. For example, Copilot Autofix reduced XSS remediation times from 180 minutes to just 22 minutes, underscoring the tool’s potential for accelerating vulnerability management. PR throughput also increased by up to 40%. However, this efficiency gain coincided with a 20–30% decline in human code review interactions, highlighting a trade-off between automation benefits and the reduced depth of manual oversight. We conclude that ACRT tools, when integrated thoughtfully into development pipelines, can deliver measurable improvements in software quality and responsiveness. However, sustained benefits require careful tuning, contextual alerting, and a hybrid review strategy that maintains human involvement to preserve long term maintainability.]]>
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