一句话：提出从需求与代码文本结构提取共识双词项，增强信息检索模型以提升需求-代码追溯链接恢复性能。
软件制品间的追溯链接对开发很有价值，但人工恢复成本高且易出错。基于信息检索（IR）的自动方法依赖制品文本相似度，而需求与代码文本质量低、数量少会降低 IR 值并制约性能。本文提出从需求与代码的文本结构中提取共现词对，即共识双词项：先依据需求文本的词性收集双词项，再用代码文本过滤，最后用这些双词项扩充 IR 输入语料并增强 IR 值计算。基于九个系统的评估表明，仅用于增强 IR 技术时，该方法在 AP 上分别比纯 IR 方法和另一基线高 21.9% 与 21.8%，在 MAP 上高 9.3% 与 7.2%；与另一种不同视角的增强策略协作时，仍比该基线在 AP 和 MAP 上高 5.9% 与 4.8%。
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.
@inproceedings{paperbot7266,
title = {Using Consensual Biterms from Text Structures of Requirements and Code to Improve IR-Based Traceability Recovery},
author = {Hui Gao and Hongyu Kuang and Kexin Sun and Xiaoxing Ma and Alexander Egyed and Patrick Mäder and Guoping Rong and Dong Shao and He Zhang},
booktitle = {ASE},
year = {2022},
doi = {10.1145/3551349.3556948}
}