Research Areas

My major research interests are in the areas of software engineering and computing education. I also work on the intersection of the two fields. I will discuss some sample research interest below.

Computing Education

Computing education research is a discipline-based research that aims to make new discoveries about teaching and learning. The research covers a wide area including software engineering, cybersecurity, AI, data, ethics, psychology of learning, ethics, informaion, and so on. My work in this field includes: investigating strategies to teach a large and diverse class, analysis of program written by novice programmers, and analysis of learner's discussion on social media platforms.

Large Language Models and Student Learning

Large Language Models such as ChatGPT and Gemini are increasingly being used by students. While these models have the potential to enhance student learning, there are concerns about their impact on academic integrity and the quality of student work. My research in this area focuses on understanding how students use LLMs for their assignments, the challenges they face, and the implications for teaching and learning in computing education.

Analysis of Software Artifacts

Software artifacts such as code, models, test cases, and so on are the primary outputs of software engineering activities. These artifacts are often complex and large, making it challenging to understand their structure, behavior, and quality. My research in this area focuses on developing novel techniques for analyzing software artifacts to extract useful information that can help improve software quality, maintainability, and evolution.

Software Evolution

Change is constant, and software engineering artifacts are continuosly updated due to changing requirements, new fetures, bug fixing, and so on. The changes applied to software artifacts might have a lot of unintended effects like introducing new errors, break dependencies, increase in code complexities, and so on. Overtime, these changes become complex, the rationale behind the changes are lost, and it may become more challenging to fix or update them to modern standards. My research explores novel approaches for investigating these changes such as stdying the impact of software changes, understanding the rationale behind the changes, and automated classification of these changes.

Publications

[2025]

• Saheed Popoola. "Empirical Analysis of Pull Requests for Google Summer of Code." arXiv preprint arXiv:2412.13120. [To Appear in SIGCITE 2025 Proceedings].
• Saheed Popoola, Ashwitha Vollem, and Kofi Nti. "What do Computing Interns Discuss Online? An Empirical Analysis of Reddit Posts." arXiv preprint arXiv:2412.13296. [To Appear in SIGCITE 2025 Proceedings].
• Taiwo Akinremi, Joel Appiah, Amir Asadi,Opetunde Ibitoye, and Saheed Popoola (2025). Driving Factors Behind Adopting Virtual Testbeds for ICS Cybersecurity Education. [To Appear in SIGCITE 2025 Proceedings]
• Saheed Popoola, Vineela Kunapareddi, and Hazem Said. "Developing and sustaining a student-driven software solutions center—An experience report." In Journal of Systems and Software, 2025.

[2024]

• Abdou Fall, and Saheed Popoola. "Educators' Perspective on the role of Computational Technology in K-12 Education." Proceedings of the 25th Annual Conference on Information Technology Education. 2024.
• Saheed Popoola, Xin Zhao, Jeff Gray, and Antonio Garcia-Dominguez. "Classifying changes to LabVIEW and simulink models via changeset metrics." Innovations in Systems and Software Engineering, 2024.
• Jones Yeboah, and Saheed Popoola. "Uncovering User Concerns and Preferences in Static Analysis Tools: a Topic Modeling Approach." 2024 2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings). IEEE, 2024.
• Jones Yeboah, and Saheed Popoola. "Analyzing User Sentiments Towards Static Analysis Tools: A Study Using User Reviews." 2024 2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings). IEEE, 2024.
• Odunayo Gabriel Adepoju, Kevin Jin, Kemi Akanbi, and Saheed Popoola. "Early Detection of Heart Disease Using Machine Learning Algorithm: Performance Analysis and Model Comparison." In 2024 2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), pp. 1-5. IEEE, 2024.
• Xin Zhao, Gurshan Rai, and Saheed Popoola. "Ask or tell: An empirical study on modeling challenges from LabVIEW community." Journal of Computer Languages. 2024.

[2023]

• Jones Yeboah, and Saheed Popoola. "Efficacy of static analysis tools for software defect detection on open-source projects." International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2023.
• Saheed Popoola, Selena Ramanayake, and Sean Wielusz. "Strategies for Teaching a Large and Technically Diverse Class." Proceedings of the 24th Annual Conference on Information Technology Education. 2023.
• Weikang Ding, and Saheed Popoola. "An Ensemble Learning Voting Technique for Network Intrusion Detection Systems." 2023.

[2022]

• Olufunsho Falowo, Saheed Popoola, Josette Riep, Victor A. Adewopo, and Jacob Koch. "Threat actors’ tenacity to disrupt: Examination of major cybersecurity incidents." IEEE Access, vol 10. 2022.
• Saheed Popoola, Xin Zhao, Jeff Gray, and Antonio Garcia-Dominguez. "Classifying changes to models via changeset metrics." In Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings, pp. 276-285. 2022.
• Saheed Popoola, Jeff Gray, Antonio Garcia-Dominguez, and Dimitris Kolovos. "Analyzing model changes with Loupe." In Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings, pp. 519-528. 2022.

[2021]

• Saheed Popoola, and Jeff Gray. "Artifact Analysis of Smell Evolution and Maintenance Tasks in Simulink Models." In 2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion, pp. 817-826. IEEE, 2021.
• Saheed Popoola, Xin Zhao, and Jeff Gray. “Evolution of Bad Smells in LabVIEW Graphical Models” In Journal of Object Technology, 2020.

[2019]

• Saheed Popoola, and Jeff Gray. “A LabVIEW Metamodel for Automated Analysis.” In 2019 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 1127-1132. IEEE, 2019.

[2017]

• Saheed Popoola, Jeffrey Carver, and Jeff Gray. “Modelling as a Service: A Survey of Existing Tools” In MDETools Workshop, ACM/IEEE 20th International Conference on Model Driven Engineering Languages and Systems, 2017.

[2016]

• Saheed Popoola, Dimitrios Kolovos and Horacio Rodriguez. “EMG: A Domain-Specific Transformation Language for Synthetic Model Generation.” In 9th International Conference on Model Transformation (ICMT), 2016.

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