The CodeLinguaNexus (CLNX) framework can be adapted to enhance the performance of Large Language Models (LLMs) in identifying vulnerabilities across various programming languages by implementing a few strategic modifications. Firstly, the naturalization techniques employed in CLNX, such as structure-level and token-level naturalization, can be tailored to accommodate the syntactic and semantic characteristics of different programming languages. For instance, languages like Java or Python have distinct paradigms and constructs that may require specific transformation rules for operators, API calls, and control flow symbols. By developing a comprehensive dictionary of language-specific symbols and their natural language equivalents, CLNX can effectively bridge the gap between code and natural language for these languages.
Secondly, the critical path identification process can be refined to consider the unique execution models and control structures prevalent in other languages. For example, languages with extensive use of asynchronous programming or functional paradigms may necessitate a different approach to identifying execution paths. Incorporating language-specific analysis tools, such as Abstract Syntax Trees (ASTs) and Control Flow Graphs (CFGs) tailored to each language, can enhance the accuracy of the critical path selection.
Lastly, leveraging community-driven datasets and vulnerability databases specific to each programming language can provide valuable insights into common vulnerabilities and their patterns. By integrating these datasets into the training and fine-tuning processes of LLMs, CLNX can improve its effectiveness in identifying vulnerabilities in a broader range of programming languages.