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.

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.

Analysis of System Models

System models such as MATLAB/Simulink or LabVIEW are widely used by traditional engineers (e.g., Electrical or Mechanical Engineers) to develop software in diverse domains. However, existing research shows that most of these engineers may not be familiar with software engineering research and tends to prioritize correctness over other properties (e.g readability, adaptation to changing requirements) that affects the maintenance of the developed software in the long term. This leads to increase in maintenance cost and the introduction of design defects in the software. This research aims to analyse changes across the version history of exisiting LabVIEW and Simulink models in GitHub repositories with the goal of reducing the design defects via appropriate recommendations to engineers developing software with both LabVIEW and Simulink,

Modeling Tools as a Service

Traditional modeling tools are often delivered as a software package that need to be installed locally or as part of a distributed software e.g eclipse plugin. Due to the complexities of these tools, they often require non-trivial configurations and inter-dependencies. This makes them hard to install, update and maintain. This local installation of these tools also limits the reuse of modeling artifacts consumed by them and new artifacts need to be built from scratch every time. Recently, there has been a lot of migration of software products to the cloud which helps remove the complexities of local maintenance and also helps to harness the computing power in the cloud. The aim of this project is to develop novel and optimal ways by which modeling tools can be migrated to the cloud and its functionalities delivered as a service.

Model-Driven Engineering

Model Driven Engineering (MDE) is a disruptive approach to software engineering whereby models are used as first class artifacts in software development in order to boost productivity. These models are manipulated by model management programs that need to be tested (because they are software and can develop bugs!) in order to boost confidence in their correctness. To test these programs, appropriate models that satisfy necessary constraints are required as test data. Models have inherently complex structures and are often required to satisfy non-trivial constraints which makes them time consuming, labour intensive and error prone to construct manually. Automated capabilities are therefore required, however, existing fully-automated model generation tools cannot generate models that satisfy arbitrarily complex constraints. This project addresses this problem by developing a framework that supports the development of model generators that can produce random and reproducible test models that satisfy complex constraints.

Publications

[2025]

• 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]

• Saheed Popoola, Ashwitha Vollem, and Kofi Nti. "What do Computing Interns Discuss Online? An Empirical Analysis of Reddit Posts." arXiv preprint arXiv:2412.13296 (2024).
• Saheed Popoola. "Empirical Analysis of Pull Requests for Google Summer of Code." arXiv preprint arXiv:2412.13120 (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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