# A Framework to Quantify the Quality of Source Code Obfuscation
## 7 Discussion  
In our study, we endeavor to establish a rigorous framework for the quantitative assessment of obfuscation techniques by leveraging specific metrics derived from static and dynamic analysis. This approach addresses a crucial gap in current research: the quantification of obfuscation quality, which traditionally leans towards qualitative assessments due to the intricate nature of software comprehension and reverse engineering practices. However, several challenges and issues need to be addressed.  
Challenges of Software Metrics in Evaluating Potency. Our research methodically measures selected software metrics to approximate the Potency of obfuscation techniques. Although these metrics intuitively reflect the structural and operational complexities introduced by obfuscation, their correlation with human comprehension remains inadequately explored. The primary aim of obfuscation is to thwart reverse engineering by complicating the comprehension process, making the software difficult to decode and analyze. Metrics such as McCabe's cyclomatic complexity, code density, and control flow alteration provide a surface-level estimation of these complexities but do not directly translate into the cognitive challenges faced by human analysts. As such, while these metrics are valuable, they capture only a facet of the obfuscation's effectiveness, neglecting the nuanced and often subjective experience of comprehending obfuscated code.  
Limitations of Control Flow Graph (CFG) Analysis. A significant technical challenge in this research concerns measuring the Control Flow Graph (CFG). CFGs are pivotal in understanding program structure and flow, particularly under static analysis. However, certain obfuscation techniques specifically target and disrupt the legibility and reconstructability of CFGs. For instance, techniques involving runtime pointer dereferences effectively obscure the true flow of control, thereby eluding traditional CFG reconstruction methods. Our framework currently does not encompass strategies to counteract or measure the impact of such techniques, which limits its ability to assess Resilience against advanced obfuscation methods fully.  
Implications for Future Research. These insights underscore the need to develop more sophisticated methods for analyzing obfuscated code, particularly in enhancing our understanding of how such techniques affect human comprehension. Future research could focus on developing empirical studies that measure the time and effort required by professional reverse engineers to decipher obfuscated code. Additionally, improving CFG analysis in the presence of obfuscation techniques that alter runtime behavior could provide a more comprehensive understanding of an obfuscation's true Potency.  
Addressing Framework Shortcomings. Future iterations of our framework must incorporate methods to address these identified shortcomings. This might include integrating cognitive psychology insights to link software metrics with human comprehension difficulty or developing advanced tools capable of dynamic CFG reconstruction despite obfuscation techniques aimed at runtime processes.  
In conclusion, while our framework sets a foundational stage for the quantitative assessment of software obfuscation, it also highlights critical areas for enhancement and further research. Bridging these gaps will improve the accuracy of our evaluations and contribute to the broader field of cybersecurity by developing tools and methodologies that keep pace with evolving obfuscation technologies.