• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Conference on New Trends in Information Science, Service Science and Data Mining >A Framework of Software Requirements Quality Analysis System using Case-Based Reasoning and Neural Network
【24h】

A Framework of Software Requirements Quality Analysis System using Case-Based Reasoning and Neural Network

机译：使用基于案例的推理和神经网络的软件要求质量分析系统框架

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this paper, we propose a new approach to Software Requirements Specifications (SRS) or software requirements quality analysis process. We apply the Software Quality Assurance (SQA) audit technique in determining whether or not the required quality standards within the requirements specifications phase are being followed closely. Quality analysis of the SRS is performed to ensure that the software requirements among others are complete, consistent, correct, modifiable, ranked, traceable, unambiguous, and understandable. Here, a new approach that combines case-based reasoning (CBR) and neural network techniques in analyzing SRS quality is proposed. This approach is used in improving the process of analyzing the quality of a given SRS document for a specific project. The CBR technique is used to evaluate the requirements quality by referring to previously stored software requirements quality analysis cases (past experiences). CBR is an artificial intelligence technique that reasons by remembering previously experienced cases, and this technique will speed up the quality analysis process. Neural Network (Artificial Neural Network or ANN) is the type of information processing paradigm that is inspired by the way biological nervous systems (brain) process information. Neural network technique works well with CBR because it also uses examples to solve problems. The new approach proposed in this research aims at enhancing and improving existing methods in analyzing SRS quality. A framework of the proposed approach is the main outcome of this research study.
机译：在本文中，我们提出了一种新的软件要求规范（SRS）或软件要求质量分析过程的方法。我们应用软件质量保证（SQA）审计技术在确定要求规格阶段的所需质量标准是否紧密遵循。执行SRS的质量分析，以确保其他人的软件要求是完整的，一致的，正确，可修改的，排名，可追溯，明确的和可理解的。这里，提出了一种结合基于案例的推理（CBR）和神经网络技术在分析SRS质量方面的新方法。这种方法用于改进分析特定项目的给定SRS文档的质量的过程。 CBR技术用于通过参考先前存储的软件需求质量分析案例（过去的经验）来评估要求质量。 CBR是一种人工智能技术，原因通过记住以前经验丰富的案例，这种技术将加快质量分析过程。神经网络（人工神经网络或ANN）是由生物神经系统（大脑）过程信息的方式启发的信息处理范式的类型。神经网络技术适用于CBR，因为它还使用示例来解决问题。本研究提出的新方法旨在提高和改进分析SRS质量的现有方法。拟议方法的框架是本研究研究的主要结果。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号