Scalable Detection of Semantic Clones in Software Code

Deep learning (DL) has shown great promise in detecting software vulnerabilities, but it lacks interpretability, making it challenging to understand the reasoning behind its predictions. To address this issue, explainability approaches can be employed to provide insights into the decision-making process of DL models. In this article, we explore the potential of utilizing explainability techniques in DL-based code analysis for vulnerability detection.

Explainability Techniques

There are three main categories of explanation techniques commonly used in graph neural networks (GNNs):

1. Numeric edge importance scores [10]: These methods assign numerical values to edges in the graph, which can be used to identify critical connections between nodes.
2. Node importance scores [12]: This approach computes importance scores for individual nodes, highlighting their relevance to the task at hand.
3. Graph walk-based explainability [13]: These methods calculate scores based on the traversal of GNNs, providing a comprehensive understanding of how the model processes information.

Insufficiencies in Existing Approaches

While explanation techniques have shown success in other domains, their application to vulnerability detection faces inherent limitations:

1. Limited capture of subtle semantics: Existing methods struggle to capture the rich semantic information encompassed by benign and vulnerable code bases, which is crucial for explaining vulnerability detection approaches.
2. Neglect of fine-grained information: Existing techniques generally neglect the fine-grained information required for understanding vulnerability detection, such as the control flow or program dependence relationships between statements.
3. Complexity of program vulnerability detection: Program vulnerability detection is inherently more complex than other tasks due to its topological structure, making it challenging to apply existing explainability techniques directly.

Solution

To overcome these limitations, we propose leveraging explanation approaches in DL-based code analysis for vulnerability detection. By selecting important features using explanation techniques and mapping them to the corresponding code lines, we can derive fine-grained information regarding vulnerabilities, such as the triggering code lines. This approach has the potential to provide a comprehensive understanding of vulnerability detection models, enabling developers to identify and fix security vulnerabilities more efficiently.

Conclusion

In conclusion, this article explores the potential of utilizing explainability techniques in DL-based code analysis for vulnerability detection. By leveraging these approaches, we can enhance the interpretability of DL models, providing valuable insights into their decision-making process and enabling developers to develop more secure software. As the field of software security continues to evolve, it is essential to combine the latest advances in AI and graph neural networks with explainability techniques to create a more robust and reliable approach for detecting vulnerabilities in code.