Gamify4LexAmb:解决自然语言需求中词汇歧义的游戏化方法

IF 3.5 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE PeerJ Computer Science Pub Date : 2024-09-19 DOI:10.7717/peerj-cs.2229
Hafsa Dar, Romana Aziz, Javed Ali Khan, Muhammad IkramUllah Lali, Nouf Abdullah Almujally
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引用次数: 0

摘要

含糊不清是指定自然语言(NL)需求时经常遇到的难题。软件需求含糊不清的原因之一是用户在需求激发和检查阶段缺乏参与。即使用户参与其中,他们也很难理解系统的上下文,最终因缺乏兴趣而无法正确提供需求。此前,研究人员曾研究过需求中的模糊性规避、检测和消除技术。然而,在需求工程的早期阶段,让用户积极参与系统以减少模糊性的文献报道仍然较少。传统上,模糊性是在 SRS 文档最初规定需求时,在检查过程中解决的。在检查过程中解决或消除模棱两可的问题费时、费钱、费力。此外,传统的诱导技术也有局限性,如缺乏用户参与、用户参与不积极、存在偏见、需求不完整等。因此,在本研究中,我们设计了一个框架--词义模糊游戏化(Gamify4LexAmb),利用游戏化来检测和减少词义模糊。Gamify4LexAmb 让用户参与进来,并识别需求中的词汇歧义,这种歧义发生在多义词中,即一个词可能有几种不同的含义。我们还通过开发初始原型验证了 Gamify4LexAmb。结果表明,Gamify4LexAmb 通过让用户参与需求激发,成功识别了给定需求中的词汇歧义。在下一部分研究中,我们将进行一项工业案例研究,以了解游戏化对检测和减少 NL 歧义的实时数据的影响。
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Gamify4LexAmb: a gamification-based approach to address lexical ambiguity in natural language requirements
Ambiguity is a common challenge in specifying natural language (NL) requirements. One of the reasons for the occurrence of ambiguity in software requirements is the lack of user involvement in requirements elicitation and inspection phases. Even if they get involved, it is hard for them to understand the context of the system, and ultimately unable to provide requirements correctly due to a lack of interest. Previously, the researchers have worked on ambiguity avoidance, detection, and removal techniques in requirements. Still, less work is reported in the literature to actively engage users in the system to reduce ambiguity at the early stages of requirements engineering. Traditionally, ambiguity is addressed during inspection when requirements are initially specified in the SRS document. Resolving or removing ambiguity during the inspection is time-consuming, costly, and laborious. Also, traditional elicitation techniques have limitations like lack of user involvement, inactive user participation, biases, incomplete requirements, etc. Therefore, in this study, we have designed a framework, Gamification for Lexical Ambiguity (Gamify4LexAmb), for detecting and reducing ambiguity using gamification. Gamify4LexAmb engages users and identifies lexical ambiguity in requirements, which occurs in polysemy words where a single word can have several different meanings. We have also validated Gamify4LexAmb by developing an initial prototype. The results show that Gamify4LexAmb successfully identifies lexical ambiguities in given requirements by engaging users in requirements elicitation. In the next part of our research, an industrial case study will be performed to understand the effects of gamification on real-time data for detecting and reducing NL ambiguity.
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来源期刊
PeerJ Computer Science
PeerJ Computer Science Computer Science-General Computer Science
CiteScore
6.10
自引率
5.30%
发文量
332
审稿时长
10 weeks
期刊介绍: PeerJ Computer Science is the new open access journal covering all subject areas in computer science, with the backing of a prestigious advisory board and more than 300 academic editors.
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