Table Of Links Table Of Links ABSTRACT ABSTRACT I. INTRODUCTION I. INTRODUCTION I. INTRODUCTION II. BACKGROUND II. BACKGROUND II. BACKGROUND III. DESIGN III. DESIGN III. DESIGN DEFINITIONS DESIGN GOALS FRAMEWORK EXTENSIONS DEFINITIONS DESIGN GOALS FRAMEWORK EXTENSIONS IV. MODELING IV. MODELING IV. MODELING CLASSIFIERS FEATURES CLASSIFIERS FEATURES V. DATA COLLECTION V. DATA COLLECTION V. DATA COLLECTION VI. CHARACTERIZATION VI. CHARACTERIZATION VI. CHARACTERIZATION VULNERABILITY FIXING LATENCY ANALYSIS OF VULNERABILITY FIXING CHANGES ANALYSIS OF VULNERABILITY-INDUCING CHANGES VULNERABILITY FIXING LATENCY ANALYSIS OF VULNERABILITY FIXING CHANGES ANALYSIS OF VULNERABILITY-INDUCING CHANGES VII. RESULT VII. RESULT VII. RESULT N-FOLD VALIDATION EVALUATION USING ONLINE DEPLOYMENT MODE N-FOLD VALIDATION EVALUATION USING ONLINE DEPLOYMENT MODE VIII. DISCUSSION VIII. DISCUSSION VIII. DISCUSSION IMPLICATIONS ON MULTI-PROJECTS IMPLICATIONS ON ANDROID SECURITY WORKS THREATS TO VALIDITY ALTERNATIVE APPROACHES IMPLICATIONS ON MULTI-PROJECTS IMPLICATIONS ON ANDROID SECURITY WORKS THREATS TO VALIDITY ALTERNATIVE APPROACHES IX. RELATED WORK IX. RELATED WORK IX. RELATED WORK CONCLUSION AND REFERENCES CONCLUSION AND REFERENCES CONCLUSION AND REFERENCES X. CONCLUSION X. CONCLUSION This paper presented a practical, preemptive security testing approach that is based on an accurate, online prediction of likely-vulnerable code changes at the pre-submit time. We presented the three types of new feature data that are effective in vulnerability prediction and evaluated their recall and precision via N-fold validation using the data from the large and important Android open source project. We also evaluated the online deployment mode, and identified the subset of feature data types that are not specific to a target project where the training data is collected and thus can be used for other projects (e.g., multi-projects setting). The evaluation results showed that our VP framework identifies ~80% of the evaluated, vulnerability-inducing changes at the pre-submit time with 98% precision and <1.7% false positive ratio. The positive results call for future researches (e.g., using advanced ML and GenAI techniques) to leverage the VP approach or framework for the upstream open source projects managed by communities and are at the same time critical for the numerous software and computer products used by several billions of users in a daily basis. The urgency of this paper stems from its potential societal benefits. Widespread adoption of ML-based approaches like the VP framework could greatly enhance our abilities to share the credibility data of open source contributors and projects. Such shared data would empower open source communities to combat threats like fake accounts (as seen in the Linux XZ util backdoor attack16). Additionally, this MLbased approach can facilitate rapid response across open source projects when long-planned attacks emerge. Sharing information across similar or downstream projects enhances preparedness and reduce response time to similar attacks. Therefore, we call for an open-source community initiative to establish a practice of sharing the credibility database of developers and projects for hardening our open source software supply-chains that numerous computer and software products depend on. REFERENCES REFERENCES [1] T. Menzies, J. Greenwald, and A. 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Author: Keun Soo Yim Author: Author: Keun Soo Yim Keun Soo Yim Keun Soo Yim This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. available on arxiv n arxiv
