LAURA: Let’s Arrange User Requirements Accurately
One of the most important skills in software engineering is the ability to specify clear (unambiguous) requirements. Students in the software engineering degree program at the University of Guayaquil present difficulties when describing user requirements (without using technical words) in an unambiguous manner. Thus, the present work proposes a serious game called LAURA to support software engineering students in developing their ability to specify unambiguous user requirements in natural language. LAURA has been conceived following the EMERGO methodology. To evaluate the effectiveness of the game when improving the students’ specifications and to collect their perceptions about the learning experience, we perform an experiment with 62 participants, who were divided into two groups: one that used the game and one that received a traditional training session. It reports that both groups improved their ability to specify unambiguous functional user requirements after the training sessions. However, the game group showed a higher preference for the activity experience and learning experience, considering LAURA as a game that stimulates their learning, maintaining attention with non-tense interactions, and recognizing it as a tool for learning to specify unambiguous uFRs. All in all, the experiment demonstrates that the proposed LAURA serious game can be a useful learning tool that produces learning results similar to those obtained with blackboard class while improving its uFR specifications in an interactive way.
Authors
- Janio Jadán-Guerrero (ORCID: https://orcid.org/0000-0002-3616-2074)
- Leonel Vasquez-Cevallos (ORCID: https://orcid.org/0000-0002-9332-0825)
- Franklin Parrales–Bravo (ORCID: https://orcid.org/0000-0002-6283-8197)
- Lorenzo Cevallos-Torres (ORCID: https://orcid.org/0000-0002-7211-2891)
- Giampaolo Delgado-Olea
- Alfredo López-Lozano
Institutions
- Central University of Ecuador (EC)
- University of Guayaquil (EC)
- Universidad Espíritu Santo (EC)
Publication Details
- Journal
- IEEE Access
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1109/access.2025.3579126
- Primary Topic
- Intelligent Tutoring Systems and Adaptive Learning
- Type
- article
- Field-Weighted Citation Impact
- 0.00