利用需求和代码文本结构中的抽象双项改进基于IR的可追溯性恢复
Using Consensual Biterms from Text Structures of Requirements and Code to Improve IR-Based Traceability Recovery
摘要：
可追溯性根据两个工件是否通过系统功能相关来批准软件工件之间的跟踪链接。这些痕迹对软件开发很有价值，但很难手动获得。为了应对昂贵的和容易出错的手动恢复，自动化的方法被提出来恢复痕迹，通过软件工件之间的文本相似性，如那些基于信息检索（IR）。然而，低质量和数量的工件文本的负面影响计算的IR值，从而极大地阻碍了基于IR的方法的性能。在这项研究中，我们提出从需求和代码的文本结构中提取共现词对（即，共识双项），以提高基于IR的可追溯性恢复。我们首先根据需求文本的词性收集一组双术语，然后通过代码文本过滤它们。然后，我们使用这些一致的双术语，既丰富了输入语料库的IR技术，提高计算的IR值。基于九个系统的评估表明，一般来说，当单独用于增强IR技术时，我们的方法可以在AP中分别超过纯IR方法和另一基线21.9%和21.8%，在MAP中分别超过9.3%和7.2%。此外，当用于从不同角度与另一种增强策略合作时，它可以在AP和MAP中分别超过该基线5.9%和4.8%。
英文摘要：
Traceability approves trace links among software artifacts based on whether two artifacts are related by system functionalities. The traces are valuable for software development, but are difficult to obtain manually. To cope with the costly and fallible manual recovery, automated approaches are proposed to recover traces through textual similarities among software artifacts, such as those based on Information Retrieval (IR). However, the low quality & quantity of artifact texts negatively impact the calculated IR values, thus greatly hindering the performance of IR-based approaches. In this study, we propose to extract co-occurred word pairs from the text structures of both requirements and code (i.e., consensual biterms) to improve IR-based traceability recovery. We first collect a set of biterms based on the part-of-speech of requirement texts, and then filter them through the code texts. We then use these consensual biterms to both enrich the input corpus for IR techniques and enhance the calculations of IR values. A nine-system-based evaluation shows that in general, when solely used to enhance IR techniques, our approach can outperform pure IR-based approaches and another baseline by 21.9% & 21.8% in AP, and 9.3% & 7.2% in MAP, respectively. Moreover, when used to collaborate with another enhancing strategy from different perspectives, it can outperform this baseline by 5.9% in AP and 4.8% in MAP.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
