International Conference Peer-reviewed

How Can You Improve Your As-is Models? Requirements Analysis Methods Meet GQM

Shoichiro Ito, Shinpei Hayashi, Motoshi Saeki

In Proceedings of the 23rd Working Conference on Requirements Engineering: Foundation for Software Quality (REFSQ 2017), pp. 95–111, Essen, Germany, February 2017.

Abstract

[Context & motivation] To develop information systems providing high business value, we should clarify As-is business processes and information systems supporting them, identify the problems hidden in them, and develop To-be information systems so that the identified problems can be solved. [Question/problem] In this development, we need a technique to support the identification of the problems, which can be seamlessly connected to the modeling techniques. [Principal ideas/results] In this paper, to define metrics to extract problems of the As-is system, following the domains specific to it, we propose the combination of Goal-Question-Metric (GQM) with existing requirements analysis techniques. Furthermore, we integrate goal-oriented requirements analysis (GORA) with problem frames approach and use case modeling to define the metrics of measuring the problematic efforts of human actors in the As-is models. This paper includes a case study of a reporting operation process at a brokerage office to check the feasibility of our approach. [Contribution] Our contribution is the proposal of using of GQM to identify the problems of an As-is model specified with the combination of GORA, use case modeling, and problem frames.

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BibTeX

@inproceedings{ito-refsq2017,
    author = {Shoichiro Ito and Shinpei Hayashi and Motoshi Saeki},
    title = {How Can You Improve Your As-is Models? Requirements Analysis Methods Meet GQM},
    booktitle = {Proceedings of the 23rd Working Conference on Requirements Engineering: Foundation for Software Quality},
    pages = {95--111},
    doi = {10.1007/978-3-319-54045-0_8},
    year = 2017,
}
Type
International Conference
Conference
REFSQ 2017
Location
Essen, Germany
Presented
February 28, 2017
Volume / Pages
pp. 95–111
Acceptance rate
24/74 (32%)
DOI
10.1007/978-3-319-54045-0_8