Eliciting Activity Requirements from Crowd Using Genetic Algorithm

Wang, Chunhui; Zhang, Wei; Zhao, Haiyan; Jin, Zhi

doi:10.1007/978-981-10-7796-8_8

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…In addition to all previously discussed studies, some have mainly focused on how to handle the crowdsourced requirements; hence, they are more concerned about the stage after gathering the requirements. The papers [54] and [55] have proposed a genetic algorithm-based approach in which the elicited requirements from the crowd are aggregated. They have used activity diagrams as a structured requirements description language in order to facilitate automated merging of requirements.…”

Section: F Crowdsourced Requirementsmentioning

confidence: 99%

Crowdsourcing Requirements Engineering: A Taxonomy-based Review

Alamer,

Alyahya,

Al-Dossari

2024

IJACSA

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Interesting insights have been found by the research community indicating that early user involvement in Requirements Engineering (RE) has a considerable association with higher requirements quality, software project success and as well boosting user loyalty. In addition, traditional RE approaches confront scalability issues and would be time consuming and expensive to be applied with contemporary applications that can be surrounded by a large crowd. Therefore, recent attention has been shed on leveraging the principle of Crowdsourcing (CS) in requirements engineering. Engaging the crowd in RE activities has been researched by several studies. Hence, we synthesize and review the literature of the knowledge domain Crowdsourcing Requirements Engineering using a proposed taxonomy of the area. A total of 52 studies were selected for review in this paper. The review aims to provide the potential directions in the area and pave the way for other researchers to understand it and find possible gaps.

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Section: F Crowdsourced Requirementsmentioning

confidence: 99%

Crowdsourcing Requirements Engineering: A Taxonomy-based Review

Alamer,

Alyahya,

Al-Dossari

2024

IJACSA

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“…This paper aims to use the genetic algorithm to solve the problem of multiple diagram synthesis. This idea originates from previous work [21] , but this paper has made important extensions at the following key points.…”

Section: Introductionmentioning

confidence: 99%

Activity Diagram Synthesis Using Labelled Graphs and the Genetic Algorithm

Wang

Jin

Zhang

et al. 2021

J. Comput. Sci. Technol.

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Many applications need to meet diverse requirements of a large-scale distributed user group. That challenges the current requirements engineering techniques. Crowd-based requirements engineering was proposed as an umbrella term for dealing with the requirements development in the context of the large-scale user group. However, there are still many issues. Among others, a key issue is how to merge these requirements to produce the synthesized requirements description when a set of requirements descriptions from different participants are received. Appropriate techniques are needed for supporting the requirements synthesis. Diagrams are widely used in industry to represent requirements. This paper chooses the activity diagrams and proposes a novel approach for the activity diagram synthesis which adopts the genetic algorithm to repeatedly modify a population of individual solutions toward an optimal solution. As a result, it can automatically generate a resulting diagram which combines the commonalities as many as possible while leveraging the variabilities of a set of input diagrams. The approach is featured by: 1) the labelled graph proposed as the representation of the candidate solutions during the iterative evolution; 2) the generalized entropy proposed and defined as the measurement of the solutions; 3) the genetic algorithm designed for sorting out the high-quality solution. Four cases of different scales are used to evaluate the effectiveness of the approach. The experimental results show that not only the approach gets high precision and recall but also the resulting diagram satisfies the properties of minimization and information preservation and can support the requirements traceability.

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Eliciting Activity Requirements from Crowd Using Genetic Algorithm

Cited by 2 publications

References 14 publications

Crowdsourcing Requirements Engineering: A Taxonomy-based Review

Crowdsourcing Requirements Engineering: A Taxonomy-based Review

Activity Diagram Synthesis Using Labelled Graphs and the Genetic Algorithm

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