  Mutation testing is vital for ensuring software quality. However, the
presence of equivalent mutants is known to introduce redundant cost and bias
issues, hindering the effectiveness of mutation testing in practical use.
Although numerous equivalent mutant detection (EMD) techniques have been
proposed, they exhibit limitations due to the scarcity of training data and
challenges in generalizing to unseen mutants. Recently, large language models
(LLMs) have been extensively adopted in various code-related tasks and have
shown superior performance by more accurately capturing program semantics. Yet
the performance of LLMs in equivalent mutant detection remains largely unclear.
In this paper, we conduct an empirical study on 3,302 method-level Java mutant
pairs to comprehensively investigate the effectiveness and efficiency of LLMs
for equivalent mutant detection. Specifically, we assess the performance of
LLMs compared to existing EMD techniques, examine the various strategies of
LLMs, evaluate the orthogonality between EMD techniques, and measure the time
overhead of training and inference. Our findings demonstrate that LLM-based
techniques significantly outperform existing techniques (i.e., the average
improvement of 35.69% in terms of F1-score), with the fine-tuned code embedding
strategy being the most effective. Moreover, LLM-based techniques offer an
excellent balance between cost (relatively low training and inference time) and
effectiveness. Based on our findings, we further discuss the impact of model
size and embedding quality, and provide several promising directions for future
research. This work is the first to examine LLMs in equivalent mutant
detection, affirming their effectiveness and efficiency.
