Probing Model Signal-Awareness via Prediction-Preserving Input Minimization
This work addresses the problem of understanding why AI models make certain predictions in source code analysis for researchers and practitioners, highlighting that current models may not be learning the intended vulnerability signals.
This paper investigates the signal awareness of AI models for source code understanding, specifically in software vulnerability detection. They developed a prediction-preserving input minimization (P2IM) approach to reduce source code to minimal snippets, revealing that models often rely on incorrect signals. Their new metric, Signal-aware Recall (SAR), showed a sharp drop in model recall from the high 90s to sub-60s across three neural network architectures and multiple datasets.
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 the vulnerability in the original code is missing in the minimal snippet, both of which the model however predicts as being vulnerable. We measure the signal awareness of models using a new metric we propose- Signal-aware Recall (SAR). We apply P2IM on three different neural network architectures across multiple datasets. 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. Although the drop in model performance may be perceived as an adversarial attack, but this isn't P2IM's objective. The idea is rather to uncover the signal-awareness of a black-box model in a data-driven manner via controlled queries. SAR's purpose is to measure the impact of task-agnostic model training, and not to suggest a shortcoming in the Recall metric. The expectation, in fact, is for SAR to match Recall in the ideal scenario where the model truly captures task-specific signals.