Added papers citing our work and our own related papers

README.md

@@ -106,6 +106,111 @@ scripts described in that paper](MSR2019)
 
 > If you wrote a paper that uses the data or the tools from this repository, please let us know (through an issue) and we'll add it to this list.
 
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+
+<!-- format :                    Author last name, Initials. (Month Year). [Article title](URL)             -->
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+**Papers citing our work**
+* Bui, Q-C. et al. (May 2022). [Vul4J: a dataset of reproducible Java vulnerabilities geared towards the study of program repair techniques](https://dl.acm.org/doi/abs/10.1145/3524842.3528482)  
+* Galvão, P.L. (October 2022). [Analysis and Aggregation of Vulnerability Databases with Code-Level Data](https://repositorio-aberto.up.pt/bitstream/10216/144796/2/588886.pdf)
+* Aladics, T. et al.  (2022). [A Vulnerability Introducing Commit Dataset for Java: an Improved SZZ Based Approach](https://real.mtak.hu/149061/1/ICSOFT_2022_41_CR-1.pdf)
+* Sharma, T. et al. (October 2021). [A Survey on Machine Learning Techniques for Source Code Analysis](https://arxiv.org/abs/2110.09610)
+* Hommersom, D. et al. (June 2024). [Automated Mapping of Vulnerability Advisories onto their Fix Commits in Open Source Repositories](https://dl.acm.org/doi/abs/10.1145/3649590)
+* Marchand-Melsom, A. et al. (June 2020). [Automatic repair of OWASP Top 10 security vulnerabilities: A survey](https://dl.acm.org/doi/abs/10.1145/3387940.3392200)
+* Sawadogo, A. D. et al. (Dec 2021). [Early Detection of Security-Relevant Bug Reports using Machine Learning: How Far Are We?](https://arxiv.org/abs/2112.10123)
+* Sun, S. et al. (Jul 2023). [Exploring Security Commits in Python](https://arxiv.org/abs/2307.11853)
+* Reis, S. et al. (June 2021). [Fixing Vulnerabilities Potentially Hinders Maintainability](https://arxiv.org/abs/2106.03271)
+* Andrade, R., & Santos, V. (September 2021). [Investigating vulnerability datasets](https://sol.sbc.org.br/index.php/vem/article/view/17213)
+* Nguyen, T. G. et al. (May 2023). [Multi-Granularity Detector for Vulnerability Fixesv](https://arxiv.org/abs/2305.13884)
+* Siddiq, M. L., & Santos, J. C. S. (November 2022). [SecurityEval dataset: mining vulnerability examples to evaluate machine learning-based code generation techniques](https://dl.acm.org/doi/abs/10.1145/3549035.3561184)
+* Sawadogo, A. D. et al. (August 2022). [SSPCatcher: Learning to catch security patches](https://link.springer.com/article/10.1007/s10664-022-10168-9)
+* Dunlap, T. et al. (July 2024). [VFCFinder: Pairing Security Advisories and Patches](http://enck.org/pubs/dunlap-asiaccs24.pdf)
+* Dunlap, T. et al. (November 2023). [VFCFinder: Seamlessly Pairing Security Advisories and Patches](https://arxiv.org/abs/2311.01532)
+* Bao, L. et al. (July 2022). [V-SZZ: automatic identification of version ranges affected by CVE vulnerabilities](https://dl.acm.org/doi/abs/10.1145/3510003.3510113)
+* Fan, J. et al. (September 2020). [A C/C++ Code Vulnerability Dataset with Code Changes and CVE Summaries](https://dl.acm.org/doi/abs/10.1145/3379597.3387501)
+* Zhang, J. et al. (January 2023). [A Survey of Learning-based Automated Program Repair](https://arxiv.org/abs/2301.03270)
+* Alzubaidi, L. et al. (April 2023). [A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications](https://link.springer.com/article/10.1186/s40537-023-00727-2)
+* Sharma, T. et al. (December 2023). [A survey on machine learning techniques applied to source code](https://www.sciencedirect.com/science/article/pii/S0164121223003291)
+* Elder, S. et al. (April 2024). [A Survey on Software Vulnerability Exploitability Assessment](https://dl.acm.org/doi/abs/10.1145/3648610)
+* Aladics, T. et al. (March 2023). [An AST-based Code Change Representation and its Performance in Just-in-time Vulnerability Prediction](https://arxiv.org/abs/2303.16591)
+* Singhal, A., & Goel, P.K. (2023). [Analysis and Identification of Malicious Mobile Applications](https://ieeexplore.ieee.org/abstract/document/10428519)
+* Senanayake, J. et al. (July 2021). [Android Mobile Malware Detection Using Machine Learning: A Systematic Review](https://www.mdpi.com/2079-9292/10/13/1606)
+* Bui, Q-C. et al. (December 2023). [APR4Vul: an empirical study of automatic program repair techniques on real-world Java vulnerabilities](https://link.springer.com/article/10.1007/s10664-023-10415-7)
+* Senanayake, J. et al. (January 2023). [Android Source Code Vulnerability Detection: A Systematic Literature Review](https://dl.acm.org/doi/full/10.1145/3556974)
+* Reis, S. et al. (June 2023). [Are security commit messages informative? Not enough!](https://dl.acm.org/doi/abs/10.1145/3593434.3593481)
+* Anonymous authors. (2022). [Beyond syntax trees: learning embeddings of code edits by combining multiple source representations](https://openreview.net/pdf?id=H8qETo_W1-9)
+* Challande, A. et al. (April 2022). [Building a Commit-level Dataset of Real-world Vulnerabilities](https://dl.acm.org/doi/abs/10.1145/3508398.3511495)
+* Wang, S., & Nagappan, N. (July 2019). [Characterizing and Understanding Software Developer Networks in Security Development](https://arxiv.org/abs/1907.12141)
+* Harzevili, N. S. et al. (March 2022). [Characterizing and Understanding Software Security Vulnerabilities in Machine Learning Libraries](https://arxiv.org/abs/2203.06502)
+* Tate, S. R. et al. (2020). [Characterizing Vulnerabilities in a Major Linux Distribution](https://home.uncg.edu/cmp/faculty/srtate/pubs/vulnerabilities/Vulnerabilities-SEKE2020.pdf)
+* Zhang, L. et al. (January 2023). [Compatible Remediation on Vulnerabilities from Third-Party Libraries for Java Projects](https://arxiv.org/abs/2301.08434)
+* Lee, J.Y.D., & Chieu, H.L. (November 2021). [Co-training for Commit Classification](https://aclanthology.org/2021.wnut-1.43/)
+* Nikitopoulos, G. et al. (August 2021). [CrossVul: a cross-language vulnerability dataset with commit data](https://dl.acm.org/doi/10.1145/3468264.3473122)
+* Bhandari, G.P. (July 2021). [CVEfixes: Automated Collection of Vulnerabilities and Their Fixes from Open-Source Software](https://arxiv.org/abs/2107.08760)
+* Sonnekalb, T. et al. (October 2021). [Deep security analysis of program code](https://link.springer.com/article/10.1007/s10664-021-10029-x)
+* Triet, H.M. et al. (August 2021). [DeepCVA: Automated Commit-level Vulnerability Assessment with Deep Multi-task Learning](https://arxiv.org/abs/2108.08041)
+* Senanayake, J. et al. (May 2024). [Defendroid: Real-time Android code vulnerability detection via blockchain federated neural network with XAI](https://www.sciencedirect.com/science/article/pii/S2214212624000449)
+* Stefanoni, A. et al. (2022). [Detecting Security Patches in Java Projects Using NLP Technology](https://aclanthology.org/2022.icnlsp-1.6.pdf)
+* Okutan, A. et al. (May 2023). [Empirical Validation of Automated Vulnerability Curation and Characterization](https://s2e-lab.github.io/preprints/tse23-preprint.pdf)
+* Wang, J. et al. (October 2023). [Enhancing Large Language Models for Secure Code Generation: A Dataset-driven Study on Vulnerability Mitigation](https://arxiv.org/abs/2310.16263)
+* Bottner, L. et al. (December 2023). [Evaluation of Free and Open Source Tools for Automated Software Composition Analysis](https://dl.acm.org/doi/abs/10.1145/3631204.3631862)
+* Ganz, T. et al. (November 2021). [Explaining Graph Neural Networks for Vulnerability Discovery](https://dl.acm.org/doi/abs/10.1145/3474369.3486866)
+* Ram, A. et al. (November 2019). [Exploiting Token and Path-based Representations of Code for Identifying Security-Relevant Commits](https://arxiv.org/abs/1911.07620)
+* Md. Mostafizer Rahman, et al. (July 2023). [Exploring Automated Code Evaluation Systems and Resources for Code Analysis: A Comprehensive Survey](https://arxiv.org/abs/2307.08705)
+* Zhang, Y. et al. (October 2023). [How well does LLM generate security tests?](https://arxiv.org/abs/2310.00710)
+* Jing, D. (2022). [Improvement of Vulnerable Code Dataset Based on Program Equivalence Transformation](https://iopscience.iop.org/article/10.1088/1742-6596/2363/1/012010)
+* Wu, Y. et al. (May 2023). [How Effective Are Neural Networks for Fixing Security Vulnerabilities](https://arxiv.org/abs/2305.18607)
+* Yang, G. et al. (August 2021). [Few-Sample Named Entity Recognition for Security Vulnerability Reports by Fine-Tuning Pre-Trained Language Models](https://arxiv.org/abs/2108.06590)
+* Zhou, J. et al. (2021). [Finding A Needle in a Haystack: Automated Mining of Silent Vulnerability Fixes](https://ieeexplore.ieee.org/abstract/document/9678720)
+* Dunlap, T. et al. (2023). [Finding Fixed Vulnerabilities with Off-the-Shelf Static Analysis](https://ieeexplore.ieee.org/document/10190493)
+* Shestov, A. et al. (January 2024). [Finetuning Large Language Models for Vulnerability Detection](https://arxiv.org/abs/2401.17010)
+* Scalco, S. et al. (July 2024). [Hash4Patch: A Lightweight Low False Positive Tool for Finding Vulnerability Patch Commits](https://dl.acm.org/doi/10.1145/3643991.3644871)
+* Nguyen-Truong, G. et al. (July 2022). [HERMES: Using Commit-Issue Linking to Detect Vulnerability-Fixing Commits](https://ieeexplore.ieee.org/abstract/document/9825835)
+* Wang, J. et al. (July 2024). [Is Your AI-Generated Code Really Safe? Evaluating Large Language Models on Secure Code Generation with CodeSecEval](https://arxiv.org/abs/2407.02395)
+* Sawadogo, A.D. et al. (January 2020). [Learning to Catch Security Patches](https://arxiv.org/abs/2001.09148)
+* Tony, C. et al. (March 2023). [LLMSecEval: A Dataset of Natural Language Prompts for Security Evaluations](https://arxiv.org/abs/2303.09384)
+* Wang, S., & Naggapan, N. (July 2019). [Characterizing and Understanding Software Developer Networks in Security Development](https://www.researchgate.net/publication/334760102_Characterizing_and_Understanding_Software_Developer_Networks_in_Security_Development)
+* Chen, Z. et al. (April 2021). [Neural Transfer Learning for Repairing Security Vulnerabilities in C Code](https://arxiv.org/abs/2104.08308)
+* Papotti, A. et al. (September 2022). [On the acceptance by code reviewers of candidate security patches suggested by Automated Program Repair tools](https://arxiv.org/abs/2209.07211)
+* Mir, A.M. et al. (February 2024). [On the Effectiveness of Machine Learning-based Call Graph Pruning: An Empirical Study](https://arxiv.org/abs/2402.07294)
+* Dietrich, J. et al. (June 2023). [On the Security Blind Spots of Software Composition Analysis](https://arxiv.org/abs/2306.05534)
+* Triet H. M. Le., & Babar, A.M. (March 2022). [On the Use of Fine-grained Vulnerable Code Statements for Software Vulnerability Assessment Models](https://arxiv.org/abs/2203.08417)
+* Chapman, J., & Venugopalan, H. (January 2023). [Open Source Software Computed Risk Framework](https://ieeexplore.ieee.org/abstract/document/10000561)
+* Canfora, G. et al. (February 2022). [Patchworking: Exploring the code changes induced by vulnerability fixing activities](https://www.researchgate.net/publication/355561561_Patchworking_Exploring_the_code_changes_induced_by_vulnerability_fixing_activities)
+* Garg, S. et al. (June 2021). [PerfLens: a data-driven performance bug detection and fix platform](https://dl.acm.org/doi/abs/10.1145/3460946.3464318)
+* Coskun, T. et al. (November 2022). [Profiling developers to predict vulnerable code changes](https://dl.acm.org/doi/abs/10.1145/3558489.3559069)
+* Bhuiyan, M.H.M. et al. (July 2023). [SecBench.js: An Executable Security Benchmark Suite for Server-Side JavaScript](https://ieeexplore.ieee.org/abstract/document/10172577)
+* Reis, S. et al. (October 2022). [SECOM: towards a convention for security commit messages](https://dl.acm.org/doi/abs/10.1145/3524842.3528513)
+* Bennett, G. et al. (June 2024). [Semgrep*: Improving the Limited Performance of Static Application Security Testing (SAST) Tools](https://dl.acm.org/doi/abs/10.1145/3661167.3661262)
+* Chi, J. et al. (October 2020). [SeqTrans: Automatic Vulnerability Fix via Sequence to Sequence Learning](https://arxiv.org/abs/2010.10805)
+* Ahmed, A. et al. (May 2023). [Sequential Graph Neural Networks for Source Code Vulnerability Identification](https://arxiv.org/abs/2306.05375)
+* Sun, J. et al. (February 2023). [Silent Vulnerable Dependency Alert Prediction with Vulnerability Key Aspect Explanation](https://arxiv.org/abs/2302.07445)
+* Zhao, L. et al. (November 2023). [Software Composition Analysis for Vulnerability Detection: An Empirical Study on Java Projects](https://dl.acm.org/doi/10.1145/3611643.3616299)
+* Zhan, Q. et al. (January 2024). [Survey on Vulnerability Awareness of Open Source Software](https://www.jos.org.cn/josen/article/abstract/6935)
+* Li, X. et al. (March 2023). [The anatomy of a vulnerability database: A systematic mapping study](https://www.sciencedirect.com/science/article/pii/S0164121223000742)
+* Al Debeyan, F. et al. (February 2024). [The impact of hard and easy negative training data on vulnerability prediction performance☆](https://www.sciencedirect.com/science/article/pii/S0164121224000463)
+* Xu, C. et al. (December 2021). [Tracking Patches for Open Source Software Vulnerabilities](https://arxiv.org/abs/2112.02240)
+* Risse, N., & Böhme, M. (June 2023). [Uncovering the Limits of Machine Learning for Automatic Vulnerability Detection](https://arxiv.org/abs/2306.17193)
+* Xu, N. et al. (July 2023). [Understanding and Tackling Label Errors in Deep Learning-Based Vulnerability Detection (Experience Paper)](https://dl.acm.org/doi/abs/10.1145/3597926.3598037)
+* Wu, Y. et al. (July 2023). [Understanding the Threats of Upstream Vulnerabilities to Downstream Projects in the Maven Ecosystem](https://ieeexplore.ieee.org/abstract/document/10172868)
+* Esposito, M., & Falessi, D. (March 2024). [VALIDATE: A deep dive into vulnerability prediction datasets](https://www.sciencedirect.com/science/article/pii/S0950584924000533)
+* Wang, S. et al. (July 2022). [VCMatch: A Ranking-based Approach for Automatic Security Patches Localization for OSS Vulnerabilities](https://ieeexplore.ieee.org/abstract/document/9825908)
+* Sun, Q. et al. (December 2022). [VERJava: Vulnerable Version Identification for Java OSS with a Two-Stage Analysis](https://ieeexplore.ieee.org/abstract/document/9978189)
+* Nguyen, S. et al. (September 2023). [VFFINDER: A Graph-based Approach for Automated Silent Vulnerability-Fix Identification](https://arxiv.org/abs/2309.01971)
+* Piran, A. et al. (March 2022). [Vulnerability Analysis of Similar Code](https://ieeexplore.ieee.org/abstract/document/9724745)
+* Keller, P. et al. (February 2020). [What You See is What it Means! Semantic Representation Learning of Code based on Visualization and Transfer Learning](https://arxiv.org/abs/2002.02650)
+
+___
+
+**Our related papers**
+* Cabrera Lozoya, R. et al. (March 2021). [Commit2Vec: Learning Distributed Representations of Code Changes](https://link.springer.com/article/10.1007/s42979-021-00566-z)
+* Fehrer, T. et al. (May 2021). [Detecting Security Fixes in Open-Source Repositories using Static Code Analyzers](https://dl.acm.org/doi/pdf/10.1145/3661167.3661217)
+* Ponta, S.E. et al. (June 2020). [Detection, assessment and mitigation of vulnerabilities in open source dependencies](https://www.semanticscholar.org/paper/Detection%2C-assessment-and-mitigation-of-in-open-Ponta-Plate/728eab7ac5ae7dd624d306ae5e1887f7b10447cc)
+* Dann, A. et al. (September 2022). [Identifying Challenges for OSS Vulnerability Scanners - A Study & Test Suite](https://www.computer.org/csdl/journal/ts/2022/09/09506931/1vNfNyyKDOo)
+* Ponta, S.E. et al. (August 2021). [The Used, the Bloated, and the Vulnerable: Reducing the Attack Surface of an Industrial Application](https://arxiv.org/abs/2108.05115)
+* Iannone, E. et al. (June 2021). [Toward Automated Exploit Generation for Known Vulnerabilities in Open-Source Libraries](https://ieeexplore.ieee.org/abstract/document/9462983)
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