International Conference Peer-reviewed

Supporting Q&A Processes in Requirements Elicitation: Bad Smell Detection and Version Control

Yui Imahori, Junzo Kato, Shinpei Hayashi, Atsushi Ohnishi, Motoshi Saeki

In Quality of Information and Communications Technology: Proceedings of the 17th International Conference on the Quality of Information and Communications Technology (QUATIC 2024), Communications in Computer and Information Science, vol. 2178, pp. 253–268, Pisa, Italy, September 2024.

Abstract

In the process of developing requirements specifications, a requirements analyst conducts question-and-answer (Q&A) sessions iteratively to incrementally make more complete initial requirements preobtained from stakeholders. However, iterated Q&A sessions often have some problems leading to a final requirements specification of lower quality. This paper presents the usage of a graph database system to identify bad smells in Q&A processes, which are symptoms leading to a lower quality product, and to control the versions of a list of requirements through the activities. In this system, the records of the Q&A activities and the requirements lists are structured and stored in a graph database Neo4j. Cypher, a database manipulation language, was used to show that we could retrieve bad smells in the Q&A process and visualize any version of the requirements list evolving through the processes.

Slides

BibTeX

@inproceedings{imahori-quatic2024,
    author = {Yui Imahori and Junzo Kato and Shinpei Hayashi and Atsushi Ohnishi and Motoshi Saeki},
    title = {Supporting {Q\&A} Processes in Requirements Elicitation: Bad Smell Detection and Version Control},
    booktitle = {Quality of Information and Communications Technology: Proceedings of the 17th International Conference on the Quality of Information and Communications Technology},
    pages = {253--268},
    doi = {10.1007/978-3-031-70245-7_18},
    year = 2024,
}
Type
International Conference
Conference
QUATIC 2024
Location
Pisa, Italy
Presented
September 12, 2024
Volume / Pages
vol. 2178, pp. 253–268
Acceptance rate
20/50 (40%)
DOI
10.1007/978-3-031-70245-7_18