通过划分源代码实现面向对象系统的需求可追溯性

Nasir Ali, Yann-Gaël Guéhéneuc, G. Antoniol
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引用次数: 30

摘要

需求跟踪能力确保源代码与文档一致,并且所有需求都已实现。在软件发展过程中,特性被添加、删除或修改,代码偏离了最初的需求。因此,跟踪能力恢复方法对于重新建立需求和源代码之间的跟踪能力关系是必要的。本文提出了一种方法(Coparvo),以补充现有的面向对象程序的跟踪能力恢复方法。Coparvo减少了由传统追踪能力恢复过程恢复的假阳性链接,从而减少了人工验证的工作量。Coparvo假设从不同实体(即类名、注释、类变量或方法签名)中提取的信息是不同的信息源,它们在需求跟踪能力中可能具有不同级别的可靠性,并且每个信息源可能充当推荐跟踪能力链接的不同专家。我们在Pooka、SIP Communicator和iTrust三个数据集上应用Coparvo来过滤通过信息检索方法(即向量空间模型)恢复的假阳性链接。结果表明,Coparvo显著提高了恢复链接的准确性,并减少了手动删除假阳性链接所需的83%的工作量。
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Requirements Traceability for Object Oriented Systems by Partitioning Source Code
Requirements trace ability ensures that source code is consistent with documentation and that all requirements have been implemented. During software evolution, features are added, removed, or modified, the code drifts away from its original requirements. Thus trace ability recovery approaches becomes necessary to re-establish the trace ability relations between requirements and source code. This paper presents an approach (Coparvo) complementary to existing trace ability recovery approaches for object-oriented programs. Coparvo reduces false positive links recovered by traditional trace ability recovery processes thus reducing the manual validation effort. Coparvo assumes that information extracted from different entities (i.e., class names, comments, class variables, or methods signatures) are different information sources, they may have different level of reliability in requirements trace ability and each information source may act as a different expert recommending trace ability links. We applied Coparvo on three data sets, Pooka, SIP Communicator, and iTrust, to filter out false positive links recovered via the information retrieval approach, i.e., vector space model. The results show that Coparvo significantly improves the of the recovered links accuracy and also reduces up to 83% effort required to manually remove false positive links.
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