EvoGPT: Leveraging LLM-Driven Seed Diversity to Improve Search-Based Test Suite Generation

Abstract

Search-Based Software Testing (SBST) is a well-established approach for automated unit test generation, yet it often suffers from premature convergence and limited diversity in the generated test suites. Recently, Large Language Models (LLMs) have emerged as an alternative technique for unit test generation. We present EvoGPT, a hybrid test generation system that integrates LLM-based test generation with SBST-based test suite optimization. EvoGPT uses LLMs to generate an initial population of test suites, and uses an Evolutionary Algorithm (EA) to further optimize this test suite population. A distinguishing feature of EvoGPT is its explicit enforcement of diversity, achieved through the use of multiple temperatures and prompt instructions during test generation. In addition, each LLM-generated test is refined using a generation-repair loop and coverage-guided assertion generation. To address evolutionary plateaus, EvoGPT also detects stagnation during search and injects additional LLM-generated tests aimed at previously uncovered branches. Here too diversity is enforced using multiple temperatures and prompt instructions. We evaluate EvoGPT on Defects4J, a standard benchmark for test generation. The results show that EvoGPT achieves, on average, a 10% improvement in both code coverage and mutation score metrics compared to TestART, an LLM-only baseline; and EvoSuite, a standard SBST baseline. An ablation study indicates that explicitly enforcing diversity both at initialization and during the search is key to effectively leveraging LLMs for automated unit test generation.