Gamify4LexAmb:一种基于游戏化的方法,用于解决自然语言要求中的词汇模两可
Hafsa Dar1, Romana Aziz2, Javed Ali Khan3
1Department of Software Engineering, University of Gujrat, Gujrat, Punjab, Pakistan.
PeerJ. Computer science
|September 24, 2024
概括
本研究介绍了Gamify4LexAmb,这是一个新的框架,使用游戏化来吸引用户并减少自然语言要求中的词汇模两可. 该系统有效地识别了模两可的术语,改善了早期的需求工程.
科学领域:
- 软件工程 软件工程 软件工程
- 自然语言处理自然语言处理.
- 人与计算机的交互
背景情况:
- 自然语言要求的模糊性是一个持续的挑战,通常源于用户参与和理解在提取过程中有限.
- 检测和解决软件要求规范 (SRS) 中的模两可的传统方法通常在开发周期的晚期执行,证明耗时且昂贵.
- 现有的研究重点是避免,检测和删除模两可,对积极的用户参与进行有限的探索,以减少早期的模两可.
研究的目的:
- 设计和开发一个框架,Gamify4LexAmb,利用游戏化来积极吸引用户识别和减少自然语言要求中的词汇模两可.
- 通过促进用户参与和兴趣来解决传统需求引发技术的局限性.
- 在需求工程的早期阶段减少模糊性,从而减轻下游开发问题.
主要方法:
- 设计了一个名为Gamify4LexAmb的框架,集成游戏化原则,以加强用户参与要求提取过程.
- 该框架特别针对词汇模两可,这种模两可的词源于具有多种含义的多种词.
- 开发和验证了Gamify4LexAmb的初始原型,以测试其在识别模两可方面的有效性.
主要成果:
- Gamify4LexAmb原型成功地识别了自然语言要求中的词汇模两可.
- 用户参与是框架在诱导阶段检测这些模两可的能力的一个关键因素.
- 结果表明,这是一种有前途的方法,可以主动地管理软件要求中的模两可.
结论:
- 游戏化提供了一种可行的和有效的方法来吸引用户,并减少自然语言要求中的词汇模两可.
- Gamify4LexAmb框架显示了在软件开发生命周期的早期提高要求的准确性和清晰性的潜力.
- 未来的研究将涉及一个工业案例研究,以进一步评估游戏化对实时模糊性检测和减少的影响.
关键词:
模两可的检测检测模两可的检测游戏元素 游戏元素游戏化就是游戏化.词法上的模两可.自然语言是一种自然语言.在PBLBL中,您可以使用PBL.这些标签是POS标签.要求的模糊性 要求的模糊性要求提醒 要求提醒用户参与 用户参与 用户参与更多相关视频
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