Requirements Elicitation in Government Projects: A Preliminary Empirical Study (2404.05425v1)
Abstract: Government development projects vary significantly from private sector initiatives in scope, stakeholder complexity, and regulatory requirements. There is a lack of empirical studies focusing on requirements engineering (RE) activities specifically for government projects. We addressed this gap by conducting a series of semi-structured interviews with 12 professional software practitioners working on government projects. These interviewees are employed by two types of companies, each serving different government departments. Our findings uncover differences in the requirements elicitation phase between government projects, particularly for data visualization aspects, and other software projects, such as stakeholders and policy requirements. Additionally, we explore the coverage of human and social aspects in requirements elicitation, finding that culture, team dynamics, and policy implications are critical considerations. Our findings also pinpoint the main challenges encountered during the requirements elicitation phase for government projects. Our findings highlight future research work that is important to bridge the gap in RE activities for government software projects.
- D. Hidellaarachchi, J. Grundy, R. Hoda, and K. Madampe, “The effects of human aspects on the requirements engineering process: A systematic literature review,” IEEE Transactions on Software Engineering, vol. 48, no. 6, 2021.
- D. Gobov and I. Huchenko, “Requirement elicitation techniques for software projects in ukrainian it: an exploratory study,” in 2020 15th Conference on Computer Science and Information Systems (FedCSIS), 2020.
- Z. S. H. Abad, M. Noaeen, and G. Ruhe, “Requirements engineering visualization: a systematic literature review,” in 2016 24th International Requirements Engineering Conference (RE), 2016.
- N. W. Kim, S. C. Joyner, A. Riegelhuth, and Y. Kim, “Accessible visualization: Design space, opportunities, and challenges,” in Computer Graphics Forum, vol. 40, no. 3, 2021.
- X. Qin, Y. Luo, N. Tang, and G. Li, “Making data visualization more efficient and effective: a survey,” The VLDB Journal, vol. 29, 2020.
- L. T. Mohammed, A. A. AlHabshy, and K. A. ElDahshan, “Big data visualization: A survey,” in International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), 2022.
- C. Lan, X. Wang, J. Ren, X. Chen, and S. Chen, “Intelligent government decision-making: A multidimensional policy text visualization analysis system,” in China Nat. Conf. on Big Data & Social Computing, 2023.
- A. Graves and J. Hendler, “Visualization tools for open government data,” in 14th conference on digital government research, 2013.
- Tableau. last accessed in march 2024. interactive government data visualizations. https://www.tableau.com/interactive-public-sector-gallery.
- T. Ishida and M. Kojima, “Implementation of a local-community issues visualization system using open data and future population projection,” in Conference on Emerging Internetworking, Data & Web Tech., 2022.
- B. Ansari, M. Barati, and E. G. Martin, “Enhancing the usability and usefulness of open government data: A comprehensive review of the state of open government data visualization research,” Government Information Quarterly, vol. 39, no. 1, 2022.
- S. Park and J. R. Gil-Garcia, “Open data innovation: Visualizations and process redesign as a way to bridge the transparency-accountability gap,” Government information quarterly, vol. 39, no. 1, 2022.
- C. R. A. Inastrilla, “Data visualization in the information society,” in Seminars in Medical Writing and Education, vol. 2, 2023.
- D. Hidellaarachchi, J. Grundy, R. Hoda, and I. Mueller, “The influence of human aspects on requirements engineering-related activities: Software practitioners’ perspective,” ACM Trans. Softw. Eng. Methodol., vol. 32, no. 5, jul 2023. [Online]. Available: https://doi.org/10.1145/3546943
- M. Fazzini, H. Khalajzadeh, O. Haggag, Z. Li, H. Obie, C. Arora, W. Hussain, and J. Grundy, “Characterizing human aspects in reviews of covid-19 apps,” in 9th IEEE/ACM International Conference on Mobile Software Engineering and Systems, 2022.
- K. Ahmad, M. Abdelrazek, C. Arora, A. A. Baniya, M. Bano, and J. Grundy, “Requirements engineering framework for human-centered artificial intelligence software systems,” Applied Soft Computing, 2023.
- C. Arora, G. John, and A. Mohamed, “Advancing requirements engineering through generative ai: assessing the role of llms.(2023),” arXiv preprint arXiv:2310.13976.
- J.-M. Horcas, J. A. Galindo, and D. Benavides, “Variability in data visualization: a software product line approach,” in 26th ACM International Systems and Software Product Line Conference-Volume A, 2022.
- J. M. Horcas, M. Pinto, and L. Fuentes, “Empirical analysis of the tool support for software product lines,” Software and Systems Modeling, vol. 22, no. 1, 2023.
- J. Walny, C. Frisson, M. West, D. Kosminsky, S. Knudsen, S. Carpendale, and W. Willett, “Data changes everything: Challenges and opportunities in data visualization design handoff,” IEEE transactions on visualization and computer graphics, vol. 26, no. 1, 2019.
- D. Hinterreiter, P. Grünbacher, and H. Prähofer, “Visualizing feature-level evolution in product lines: A research preview,” in Requirements Engineering: Foundation for Software Quality: 26th International Working Conference, (REFSQ), 2020.
- X. Zhang, L. Liu, Y. Wang, X. Liu, H. Wang, A. Ren, and C. Arora, “Personagen: A tool for generating personas from user feedback,” in 31st International Requirements Engineering Conference (RE), 2023.
- L. Bulej, T. Bureš, P. Hnětynka, V. Čamra, P. Siegl, and M. Töpfer, “Ivis: Highly customizable framework for visualization and processing of iot data,” in 2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), 2020.
- Z. S. H. Abad, M. Noaeen, and G. Ruhe, “Requirements engineering visualization: A systematic literature review,” in 2016 IEEE 24th International Requirements Engineering Conference (RE), 2016.
- A. Gottfried, C. Hartmann, and D. Yates, “Mining open government data for business intelligence using data visualization: A two-industry case study,” Journal of Theoretical and Applied Electronic Commerce Research, vol. 16, no. 4, 2021.
- R. Matheus, M. Janssen, and D. Maheshwari, “Data science empowering the public: Data-driven dashboards for transparent and accountable decision-making in smart cities,” Government Information Quarterly, vol. 37, no. 3, 2020.
- A. P. Chokki, A. Simonofski, B. Frénay, and B. Vanderose, “Engaging citizens with open government data: The value of dashboards compared to individual visualizations,” Digital Government: Research and Practice, vol. 3, no. 3, 2022.
- C. Z. Li, Z. Chen, F. Xue, X. T. Kong, B. Xiao, X. Lai, and Y. Zhao, “A blockchain-and iot-based smart product-service system for the sustainability of prefabricated housing construction,” Journal of Cleaner Production, vol. 286, 2021.
- Zenodo. last accessed in march 2024. interview guide. https://zenodo.org/records/10645078.
- iflytek. last accessed in january 2024. iflytek. https://global.xfyun.cn/.
- Maxqda. last accessed in january 2024. maxqda. https://www.maxqda.com/.
- D. S. Cruzes and T. Dyba, “Recommended steps for thematic synthesis in software engineering,” in 2011 international symposium on empirical software engineering and measurement, 2011.
- Anqi Ren (2 papers)
- Lin Liu (190 papers)
- Yi Wang (1038 papers)
- Xiao Liu (402 papers)
- Hailong Wang (74 papers)
- Kaijia Xu (2 papers)
- Xishuo Zhang (2 papers)
- Chetan Arora (79 papers)