Advancements in Automated Code Scanning Techniques for Detecting Security Vulnerabilities in Open Source Software

Abstract

Purpose: This article aims to shed light on the transformative role of Open Source Software (OSS) in digital infrastructure and the accompanying security challenges. It highlights the critical need for automated code scanning technologies to address vulnerabilities stemming from coding errors, lack of secure coding practices, and the rapid development pace.
 Methodology: Through a comprehensive analysis of static, dynamic, and interactive code scanning methods, along with the exploration of AI and ML integration, this study examines scalable and efficient approaches to enhance detection capabilities early in the development lifecycle.
 Findings: While automated code scanning technologies have made significant strides in detecting and mitigating vulnerabilities, there remain notable research and methodology gaps, especially in technology scalability and the effectiveness of these methods.
 Unique Contribution to Theory, Policy, and Practice: This article posits a forward-looking perspective on automated code scanning, advocating for intelligent, collaborative, and integrated security measures in OSS. It emphasizes the indispensable role of community collaboration and open-source contributions in advancing these technologies, crucial for the proactive identification and mitigation of security vulnerabilities, thereby safeguarding the digital ecosystem's integrity and reliability.