@inproceedings{li-etal-2026-beyond-superficial,
    title = "Beyond Superficial Tests: Adversarial Refinement for Reliable Property-Based Testing",
    author = "Li, Xiao  and
      Liu, Runlin  and
      Zhang, Zhe  and
      Gao, Xiang  and
      Sun, Hailong",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.683/",
    pages = "13957--13974",
    ISBN = "979-8-89176-395-1",
    abstract = "Large Language Models (LLMs) have demonstrated remarkable proficiency in code generation, yet their application to Property-Based Testing (PBT) remains fraught with a superficiality gap. While LLMs can readily generate syntactically correct tests, they often struggle to bridge the semantic gap between code implementation and its intended invariant logic, resulting in weak properties that provide a false sense of security. To address this, we introduce PROBE, an agentic framework that hardens software properties through Adversarial Refinement. Unlike traditional generation approaches, PROBE treats test generation as a game of semantic asymmetry: it employs a Validator agent to actively generate counter-implementations, which are semantically incorrect codes that satisfy the generated property, to expose loopholes in the specification. Furthermore, PROBE constructs a cross-functional semantic graph to capture deep dependencies often missed by local analysis. Extensive evaluation reveals that PROBE increases mutation scores by 9.79{\%} over baselines. In real-world deployment, PROBE identified 45 previously unknown bugs in top-tier libraries that have been confirmed by developers, demonstrating its ability to uncover deep semantic defects."
}