From Prompts to Pareto Fronts: Using Code LMs as Semantic Genetic Operators for Compiler Bug Discovery
Differential compiler testing requires automatically generated programs that are not only diverse and bug-revealing, but also semantically well-defined and reproducible. Rule-based generators provide strong validity guarantees but offer limited control over semantic variation, while large language models (LLMs) can synthesize expressive programs without principled mechanisms for balancing competing testing objectives. This paper proposes LMOEC, a constrained multi-objective evolutionary framework that integrates code language models as semantic genetic operators within an NSGA-II search process. Instead of using the LLM as a one-shot generator, we employ it for population initialization, crossover, and mutation at the program level, enabling semantics-aware recombination while preserving strict admissibility constraints. Compiler test generation is formulated as a multi-objective optimization problem that simultaneously promotes structural diversity, cross-configuration output inconsistency, semantic complexity, and robustness to mutation. A constraint-driven acceptance pipeline enforces syntactic validity, deterministic execution, bounded runtime, and avoidance of undefined behavior before evolutionary selection. By maintaining a Pareto front of non-dominated programs, LMOEC preserves multiple high-value test archetypes reflecting different trade-offs between bug exposure and reproducibility. The framework demonstrates how expressive code models can be systematically embedded into evolutionary multi-objective optimization for reliability-critical software testing.