  In recent years, code security has become increasingly important, especially
with the rise of interconnected technologies. Detecting vulnerabilities early
in the software development process has demonstrated numerous benefits.
Consequently, the scientific community started using machine learning for
automated detection of source code vulnerabilities. This work explores and
refines the CVEFixes dataset, which is commonly used to train models for
code-related tasks, specifically the C/C++ subset. To this purpose, the Source
Code Processing Engine (SCoPE), a framework composed of strategized techniques
that can be used to reduce the size and normalize C/C++ functions is presented.
The output generated by SCoPE was used to create a new version of CVEFixes.
This refined dataset was then employed in a feature representation analysis to
assess the effectiveness of the tool's code processing techniques, consisting
of fine-tuning three pre-trained LLMs for software vulnerability detection. The
results show that SCoPE successfully helped to identify 905 duplicates within
the evaluated subset. The LLM results corroborate with the literature regarding
their suitability for software vulnerability detection, with the best model
achieving 53% F1-score.
