FRUIT: Fuzzy Representation of Unbounded and Imprecise Terms in Non-Functional Requirements
It is challenging to verify Non-Functional Requirements (NFRs) because the traditional verification process typically uses the binary concept of true or false to determine whether a requirement has been fulfilled, leaving no space for uncertainty, even in the slightest, such as partially positive or...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10776957/ |
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| Summary: | It is challenging to verify Non-Functional Requirements (NFRs) because the traditional verification process typically uses the binary concept of true or false to determine whether a requirement has been fulfilled, leaving no space for uncertainty, even in the slightest, such as partially positive or partially negative evaluations. Therefore, it is necessary to consider fuzzy logic to provide the opportunity to verify NFRs in a more flexible manner. To do this, the present manuscript exposes a methodology called the Fuzzy Representation of Unbounded and Imprecise Terms (FRUIT). It expands the PROSE structure, which we first presented in our earlier work, to enable the defining of user NFRs in natural language (NL) when considering fuzzy logic to make more flexible the evaluation of quantitative NFRs and enable the evaluation of qualitative NFRs. To define NFRs under the FRUIT methodology, we need to select the system property, metrics (linguistic variables), and fuzzy set that enable the evaluation of the NFR. A table containing attributes, metrics, and linguistic values is supplied to aid in its definition; the stakeholders and elicitors are tasked with defining the boundaries of each linguistic value. To evaluate the FRUIT methodology, 44 group projects from the required engineering courses offered between 2022 and 2024 at the University of Guayaquil were looked at by experts. According to the results, students decreased the proportion of bad NFRs from approximately 80% to 10%. In conclusion, the results show how important the FRUIT methodology is for helping students to define NFRs in NL while making more flexible the evaluation of NFRs. |
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| ISSN: | 2169-3536 |