An Approach to Characterize the Security of Open-Source Functions using LSP

The malicious exploitation of security flaws by attackers can lead to a range of problems. While several techniques and tools allow detecting vulnerabilities during the Software Development Life Cycle (SDLC), most face the challenge of generating many false alarms while failing to detect vulnerabilities. This leads software development teams to waste considerable time in the analysis and to deploy potentially vulnerable code. In this context, we believe that complementary solutions are needed to help teams focusing the vulnerability detection and refactoring efforts on the code units that are more prone to have security issues. In this paper, we propose SCOLP (Security Characterization of Open-Source Functions using Logic Scoring of Preference (LSP)), an approach based on Multi-Criteria Decision Making (MCDM), aimed at categorizing code units (functions) based on the perceived proneness to have security issues. In practice, we use static information (e.g., Software Metrics (SMs) and memory management-related attributes) collected from the source code to feed Quality Models (QMs) that output a score that is then used to categorize the code units. To demonstrate SCOLP, we developed several QMs and applied them to the functions of a large open-source project developed using the C language (Linux Kernel). A preliminary validation with security experts was conducted to assess the accuracy of the categorization. Despite the subjectiveness of the process, results show that the output of SCOLP is aligned with the view of security experts. Our technique can be easily integrated into the SDLC without adding overhead to the development processes.