Roland Croft

Roland Croft

Higher Degree by Research Candidate

School of Computer and Mathematical Sciences

Faculty of Sciences, Engineering and Technology

Roland Croft is currently a PhD student at the University of Adelaide, where he also completed his Bachelor's degree. He graduated with first class honours and was awarded the University Medal for achieving the highest academic score in his year. During his undergraduate study he completed several research projects in the field of cybersecurity. His research interests lie in natural language processing, cybersecurity, machine learning and data mining. His research aims to utilise open source security data and intelligence to create tools and frameworks for automated vulnerability analytics and prediction.

As a member of the Centre for Research on Engineering Software Technologies (CREST, https://crest-centre.net/), my research aims to develop tools for constructing and analysing dependable and secure software intensive systmes. In particular, my research aims to investigate the use of Machine Learning (ML) and Natural Language Processing (NLP) for the automatic analysis and prediction of vulnerabilities in software systems. We make use of Open-Source Intelligence (such as NVD, CWE, and Stack Overflow) to help build these tools and models. 

  • Journals
    Year Citation
    2023 Croft, R., Xie, Y., & Babar, M. A. (2023). Data Preparation for Software Vulnerability Prediction: A Systematic Literature Review. IEEE Transactions on Software Engineering, 49(3), 1044-1063.
    DOI WoS1
    2022 Croft, R., Xie, Y., Zahedi, M., Babar, M. A., & Treude, C. (2022). An empirical study of developers’ discussions about security challenges of different programming languages. Empirical Software Engineering, 27(1), 52 pages.
    DOI Scopus4 WoS2

  • Conference Papers
    Year Citation
    2022 Le, T., Hin, D., Croft, R., & Babar, M. (2022). DeepCVA: Automated Commit-level Vulnerability Assessment with Deep Multi-task Learning. In Proceedings - 2021 36th IEEE/ACM International Conference on Automated Software Engineering, ASE 2021 (pp. 717-729). online: IEEE.
    DOI Scopus7 WoS4
    2022 Croft, R., Babar, M. A., & Li, L. (2022). An Investigation into Inconsistency of Software Vulnerability Severity across Data Sources. In Proceedings - 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2022 (pp. 338-348). Online: IEEE COMPUTER SOC.
    DOI Scopus3
    2021 Croft, R., Newlands, D., Chen, Z., & Babar, A. M. (2021). An empirical study of rule-based and learning-based approaches for static application security testing. In International Symposium on Empirical Software Engineering and Measurement (pp. 1-12). New York, United States: ACM.
    DOI Scopus8
    2021 Le, T. H. M., Croft, R., Hin, D., & Babar, M. A. (2021). A large-scale study of security vulnerability support on developer Q&A websites. In Proceedings of the Evaluation and Assessment in Software Engineering Conference (EASE 2021) (pp. 109-118). New York, United States: Association for Computing Machinery.
    DOI Scopus7 WoS5
    2020 Le, T. H. M., Hin, D., Croft, R., & Babar, M. A. (2020). PUMiner: Mining security posts from developer question and answer websites with PU learning. In MSR '20: Proceedings of the 17th International Conference on Mining Software Repositories (MSR 2020) (pp. 350-361). New York, NY: ACM.
    DOI Scopus14
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