RIP-Guided Graph Evidence for LLM-Based Equivalent Mutant Detection
In recent years, prior work has explored leveraging large language models (LLMs) for equivalent mutant detection. However, representations that rely solely on source-code sequences or abstract syntax trees (ASTs) make it difficult to directly capture—and to exploit in a controllable manner—the semantic information required for equivalence reasoning, such as control dependencies, data dependencies, and path constraints. To address this limitation, we propose an RIP (Reach–Infect–Propagate)-guided, LLM-assisted approach for equivalent mutant detection. We first construct local control-flow graphs (CFGs) and data-flow graphs (DFGs) on the intermediate representations of both the original program and its mutant, and align the subgraphs relevant to the mutation location. We then, from the RIP perspective, statically extract RIP-stage evidence (e.g., path predicates, def–use chains, and control/data dependencies) to form an interpretable graph-evidence representation. Finally, we serialize the paired graph evidence into structured prompts or instruction-style inputs to the LLM, enabling it to classify mutants as equivalent or non-equivalent. Experiments on a dataset built from 10 open-source Java projects show that, compared with multiple classes of classic equivalent mutant detection techniques, our approach improves F1 by 4.81–64.34 percentage points and maintains its advantage under cross-project evaluation. In addition, injecting RIP-structured evidence consistently improves F1 over the code-only setting by roughly 8–12 percentage points.