One of the primary limitations of CLNX's structure-level naturalization is its tendency to prioritize program length reduction, which can inadvertently lead to the omission of critical code information. This reduction strategy may result in a decrease in recall, as important vulnerability-related details might be lost when selecting the shortest execution path. To address this limitation, a more nuanced approach to critical path selection could be implemented.
For instance, incorporating data flow analysis alongside control flow analysis could provide a more comprehensive understanding of how data moves through the program. By considering both the execution paths and the data dependencies, CLNX could identify paths that not only cover the maximum number of vulnerability-related basic blocks but also retain essential context that may be relevant for vulnerability detection.
Additionally, enhancing the algorithm to evaluate multiple paths with similar coverage could help in selecting paths that maintain a balance between length and the richness of information. Implementing a scoring system that weighs the importance of different basic blocks based on their historical association with vulnerabilities could further refine the critical path selection process. This would ensure that while the model remains efficient, it does not sacrifice the depth of analysis required for effective vulnerability identification.