<![CDATA[The Essay Analytic Online (ESAO) application is a smart essay evaluation system based on Artificial Intelligence (AI) that processes large text volumes. The operational reliance on the backend REST API architecture poses challenges related to performance stability and functional integrity, risking server downtime during massive access if left untested. This research aims to evaluate the backend REST API response quality of the ESAO application using the Bruno automation software. The evaluation is focused on fulfilling the Functional Suitability and Performance Efficiency characteristics based on the ISO/IEC 25010 international standard framework. A quantitative experimental approach was applied through the Automated Black Box Testing method by executing six endpoint samples representing various computational workloads for 30 test iterations. The results indicate a 100% functional success rate without any failures in returning HTTP 200 OK status codes and JSON response body integrity. In performance efficiency metrics, the server demonstrated stable response time resilience with an average of under 200 ms for light loads, 693.7 ms for medium loads, and reaching a maximum time of 6,600 ms for extreme loads without encountering any errors. In conclusion, the ESAO backend REST API architecture is proven to be robust, stable, and fully compliant with ISO/IEC 25010 standards, whereby Bruno is also proven to be highly effective as a local file-system-based automated testing instrument.]]>
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