Recovering Traceability Links Using Syntactic Analysis
-
摘要: 软件需求跟踪已被公认为影响软件项目成败的一个关键因素.针对大多数基于信息检索的需求跟踪方法都严重依赖于软件制品中的文本质量，提出了一种基于句法分析的动态需求跟踪方法.该方法能够从制品中抽取最有可能刻画自身特征的标引词，并减少制品中噪音对需求跟踪带来的不利影响.为了验证该方法的有效性，在多个来自不同项目且类型不同的软件制品上，比较了基于不同标引词集合的动态需求跟踪方法所建立的跟踪关系.实验结果表明，基于句法分析的动态需求跟踪方法能够有效地提高跟踪关系的准确性.Abstract: Software requirement traceability has been globally recognized as a key factor of affecting the success of software projects. The generated traceability links are vital to many software engineering and software verification and validation (V&V) activities such as change impact analysis, software reuse and consistency checking. Addressing most existing requirement traceability approaches based on information retrieval are strongly affected by the quality of the documentation of different types of software artifacts, this paper presents a dynamic requirement traceability approach based on syntactic analysis. The proposed approach is able to extract terms which are most likely to characterize itself from text-based software artifacts such as source code and requirement document, and then reduce the adverse effects of noise in artifacts to the requirement tracing process. In order to verify the effectiveness of the proposed approach, we have compared the quality of the trace links produced by several dynamic requirement traceability approaches on three open source software systems and six types of software artifacts. The result suggests that the dynamic requirement traceability approach based on syntactic analysis can effectively improve the accuracy of the produced trace links.
-
-
期刊类型引用(3)
1. 苏兆品，张羚，张国富. 低比特率语音流大容量分层隐写方法. 中国图象图形学报. 2022(12): 3461-3475 . 
百度学术
2. 李丽惠. 云计算环境下的数据安全传输方式研究. 漳州职业技术学院学报. 2020(04): 80-86 . 百度学术
3. 廖克顺. 基于抗转码视频处理技术的图像隐写算法. 广西师范学院学报(自然科学版). 2019(02): 50-54 . 百度学术
其他类型引用(2)
计量
- 文章访问数:
- HTML全文浏览量: 0
- PDF下载量:
- 被引次数: 5
下载:
