Impact of Information Systems
Artificial Intelligence Strategy and Governance
Digitalization
Software Development and Assurance
Shi, Y., Gebauer, J., Kline, D. M., & Gillenson, M. L. (2024). Teaching a Report-Oriented Business Intelligence Course: A Pedagogical Experience. Journal of Information Systems Education, 35(1), 73-85.
As the demand for business intelligence (BI) professionals continues to grow, educators need to calibrate their instruction to accommodate the demand of practitioners for specific technical skills while also providing college students with a broader foundation that includes a general understanding of BI concepts and problem-solving skills that are applicable across disciplines. This paper describes a pedagogical method called report-oriented learning which seeks to combine the established methods of problem-based learning and case-based learning. Report-oriented learning requires students to reflect on the knowledge gained during the conceptual parts of the course and use critical thinking and storytelling skills as they prepare and present several comprehensive reports in class. We applied the report-oriented method in a business intelligence course that consists of four instructional approaches: (1) section preview, (2) lectures and quizzes on basic concepts, (3) application of concepts and development of practical skills with hands-on projects, and (4) comprehensive reflection and inquiry in the form of reports. We surveyed students with anonymous questionnaires in the report-oriented BI courses from 2021-2023. The results indicate that the method was effective and perceived by students as having improved their critical thinking and practical skills related to the application of BI techniques and the professional presentation of their findings.
Shi, Y., Gillenson, M. L., & Zhang, X. (2022). A Quantitative Function for Estimating the Comparative Values of Software Test Cases. Journal of Database Management, 33(1), 1-33.
Software testing is becoming more critical to ensure that software functions properly. As the time, effort, and funds invested in software testing activities have been increased significantly, these resources still cannot meet the increasing demand of software testing. Managers must allocate testing resources to the test cases effectively in uncovering important defects. This study builds a value function that can quantify the relative value of a test case and thus play a significant role in prioritizing test cases, addressing the resource constraint issues in software testing and serving as a foundation of AI for software testing. The authors conducted a Monte Carlo simulation to exhibit application of the final value function.
Gillenson, M. L., Stafford, T. F., Zhang, X. & Shi, Y. (2020). Use of Qualitative Research to Generate a Function for Finding the Unit Cost of Software Test Cases. Journal of Database Management, 31(2), 42-63.
In this article, we demonstrate a novel use of case research to generate an empirical function through qualitative generalization. This innovative technique applies interpretive case analysis to the problem of defining and generalizing an empirical cost function for test cases through qualitative interaction with an industry cohort of subject matter experts involved in software testing at leading technology companies. While the technique is fully generalizable, this article demonstrates this technique with an example taken from the important field of software testing. The huge amount of software development conducted in today’s world makes taking its cost into account imperative. While software testing is a critical aspect of the software development process, little attention has been paid to the cost of testing code, and specifically to the cost of test cases, in comparison to the cost of developing code. Our research fills the gap by providing a function for estimating the cost of test cases.
Gillenson, M. L., Totty, S., Zhang, Q., Zhang, X. & Shi, Y. (2020). Global Index for Test and Evaluation Data (GIFTED): An Implementation Approach. Journal of Information Technology Management, 31(2), 22-37.
Software testing is a complex process that involves many types of data. Managing such data is difficult but crucial for improved software testing effectiveness and efficiency. Taking a broader view of “data,” we identify eleven kinds of data that must be considered in software testing. This paper first presents a conceptual framework for developing data design and management techniques in the software testing environment. It then presents a new data storage, cross-referencing, and indexing system, the Global Index for Test and Evaluation Data (GIFTED), with three possible implementations, in which to store and access the eleven kinds of data we identify in the software testing environment. The first implementation is a relational database. The second is a novel use of a graph database. The third is a federated database. This novel approach to managing software testing data, both in its breadth and in its implementation approaches, is a substantial advance in the science and practice of software testing, which can play a critical role in improving the effectiveness and efficiency of software testing.
Gillenson, Mark L., Stafford, Thomas F., Zhang, Xihui, and Shi, Yao (2022). Use of Qualitative Research to Generate a Function for Finding the Unit Cost of Software Testing Cases. In Information Resources Management Association (Ed.), Research Anthology on Agile Software, Software Development, and Testing (pp. 836-860). Hershey, PA: IGI Global. ISBN-10: 1668437023, ISBN-13: 978-1668437025, EISBN-13: 978-1668437032.
Dobrynin, D., Shi, Y., Cummings, J., & Dogan, G. (2025). Building A Hybrid Recommender System. Proceedings of the Americas Conference on Information Systems (AMCIS 2025), Montreal, Canada, August 14-16, 2025.
Jenna, B., Shi, Y., & Gebauer, J. (2024). The Role of Trust in AI Integration in Education: Factors, Issues, and Strategic Approaches. Proceedings of ISCAP 2024, Baltimore, Maryland, USA, November 6-9, 2024.
Shi, Y., Gebauer, J., & Javadi, E. (2024). A Framework of AI Strategy. Proceedings of the 57th Hawaii International Conference on System Sciences (HICSS-57), Honolulu, Hawaii, USA, January 3-6, 2024.
Poosapati, P., Shi, Y., Gebauer, J., & Song, Y. (2023). An Application of Co-plot Analysis: A Multidimensional Scaling Data Visualization. Proceedings of the 2022 5th International Conference on Machine Learning and Natural Language Processing, Sanya, Hainan, China, December 27-29, 2023.
Poosapati, P., Shi, Y., Gebauer, J., & Song, Y. (2023). Applications Of Multidimensional Scaling with Co-plot Analysis. Proceedings of ISCAP 2023, Albuquerque, New Mexico, USA, November 1-4, 2023.
Rachuri, S., Ricanek, K., Shi, Y., & Ahmed, E. (2023). An Analysis of Generative AI in Computing Education. Proceedings of the Americas Conference on Information Systems (AMCIS 2023), Panama City, Panama, August 10-12, 2023.
Shi, Y., & Gebauer, J. (2022). Using Co-plot Method to Explore IS/IT Concentrations in Top-Ranked MBA Programs in the U.S.: A Work-in-progress Study. Proceedings of ISCAP 2022, Clearwater Beach, Florida, USA, November 2-5, 2022.
Shi, Y., Gebauer, J., & Javadi, E. (2022). Role of AI Strategy in Business Transformation. Proceedings of the 28th Americas Conference on Information Systems (AMCIS 2022), Minneapolis, USA, August 10-14, 2022.
Shi, Y. (2021). A Pedagogical Experience in Designing and Teaching a Report-Oriented Business Intelligence Course. Proceedings of ISCAP 2021, Washington DC, USA, November 3-6, 2021.
Uygun, A., Kline, D., Shi, Y., & Vetter, R. (2021). Exploring Low Code Development with the Power Platform. Proceedings of ISCAP 2021, Washington DC, USA, November 3-6, 2021.
Shi, Y., & Gebauer, J. (2021). A Co-Plot Study of IS/IT Concentrations in Top-Ranked MBA Programs in the U.S. Proceedings of the 27th Americas Conference on Information Systems (AMCIS 2021), Montreal, Canada, August 10-12, 2021.
Shi, Y., Gillenson, M. L., & Zhang, X. (2019). Value Estimation of Software Functional Test Cases. Proceedings of 25th Americas Conference on Information Systems (AMCIS 2019), Cancún, Mexico, August 15-17, 2019.
Gillenson, M. L., Zhang, X., Stafford, T. F., & Shi, Y. (2018). A Literature Review of Software Test Cases and Future Research. Proceedings of 29th IEEE International Symposium on Software Reliability Engineering (ISSRE 2018), Memphis, Tennessee, USA, October 15-18, 2018.
Gillenson, M. L., Zhang, X., Stafford, T. F., & Shi, Y. (2018). Value Estimation of a Software Test Case. Proceedings of 11th International Research Workshop on Advances and Innovations in Software Testing (IRWAIST 2018), Memphis, Tennessee, USA, October 15, 2018.
Shi, Y., Booth, R. E., & Simon, J. (2017). The Iterative Effect of IT Identity on Employee Cybersecurity Compliance Behaviors. Proceedings of the 23rd Americas Conference on Information Systems (AMCIS 2017), Boston, USA, August 10-12, 2017.
Gillenson, M. L., Shi, Y., Zhang, X., & Stafford, T. F. (2017). Unit Value of a Test Case: An Update. Proceedings of 10th International Research Workshop on Advances and Innovations in Software Testing (IRWAIST 2017), Memphis, Tennessee, USA, May 1-2, 2017.
Gillenson, M. L., Shi, Y., Zhang, X., & Stafford, T. F. (2015). Unit Value of a Test Case. Proceedings of 9th International Research Workshop on Advances and Innovations in Software Testing (IRWAIST 2015), Memphis, Tennessee, USA, October 19-20, 2015.
Gillenson, M. L., Stafford, T. F., Zhang, X., Dhaliwal, J. S., & Shi, Y. (2014). Unit Cost of a Functional Test Case. Proceedings of 8th International Research Workshop on Advances and Innovations in Software Testing (IRWAIST 2014), Memphis, Tennessee, USA, November 3-4, 2014.
UNCW Research Momentum Fund (2026)
ChatGPT and Generative AI for Education, Local Government, and Business. University of North Carolina Wilmington, (group funding, 2024)
Cameron School of Business Research Grant (2021, 2022, 2024, 2025)
Charles & Edna Neumann Fund (2017)