This work explores the signal awareness of AI models for source code understanding. Using a software vulnerability detection use-case, we evaluate the models' ability to capture the correct vulnerability signals to produce their predictions. Our prediction-preserving input minimization (P2IM) approach systematically reduces the original source code to a minimal snippet which a model needs to maintain its prediction. The model's reliance on incorrect signals is then uncovered when a vulnerability in the original code is missing in the minimal snippet, both of which the model however predicts as being vulnerable. We apply P2IM on three state-of-the-art neural network models across multiple datasets, and measure their signal awareness using a new metric we propose- Signal-aware Recall (SAR). The results show a sharp drop in the model's Recall from the high 90s to sub-60s with the new metric, highlighting that the models are presumably picking up a lot of noise or dataset nuances while learning their vulnerability detection logic.

本研究探讨了AI模型对源代码理解的信号意识。使用软件漏洞检测用例，评估模型捕获正确漏洞信号以生成预测的能力，并通过预测保留输入的最小段落，揭示模型在学习漏洞检测逻辑时捕获的错误信号。通过提出的“信号感知召回率”（SAR）进行模型信号感知度的测量，并在多个数据集上应用P2IM三种不同的神经网络架构。结果表明，模型的SAR从90多陡峭下降到低于60，突显了模型在学习漏洞检测逻辑时可能会捕获很多噪声或数据集细微差异。

通过保持预测来最小化输入探测模型的信号感知能力