A Context-Aware Platform for Evaluating LLM-Generated Backend Applications

Abstract

Large Language Models (LLMs) are now widely used for code generation, yet even syntactically correct output may contain logical and semantic errors that remain invisible until runtime, particularly in framework-driven applications, where correctness depends on dependency injection, framework conventions, configuration, library compatibility, and database interaction. This paper presents GenTest, an open-source platform for context-aware dynamic generation of Java code using LLMs, runtime compilation and class loading, Spring bean registration, JPA/PostgreSQL execution, and declarative YAML-based test validation. GenTest supports multiple LLM providers through a provider-agnostic interface and evaluates generated components within a live Spring Boot application context. Across 209 test cases and 933 assertions, GenTest achieves a 63.9% assertion pass rate, and 94.2% of failures occur after successful compilation, confirming that execution-centered evaluation reveals failure modes invisible to static analysis.