基于图挖掘扩展学习的增强需求跟踪恢复方法

Enhancing Requirements Traceability Recovery via a Graph Mining-Based Expansion Learning

在软件开发全生命周期中,需求跟踪在管理需求及其相关制品方面扮演着重要的角色.由于手工跟踪费时且易出错,一些基于信息检索(information retrieval,IR)和基于机器学习(machine learning,ML)的解决方案被提出.其中,不需要大量标签数据的无监督的机器学习方法越来越受到关注.在已提出的解决方案中,大多数都是针对词法和语义信息进行建模,而忽略了文本制品间的词共现分布和词序信息.因此,提出利用基于图挖掘扩展学习的增强需求跟踪链接恢复方法GeT2Trace.其核心思想是利用图网络中的词共现信息和词序信息来增强制品中隐含的语义信息,进而更全面、更准确地对制品中所包含的语义进行表示.在5个公共数据集上进行了评估,结果表明提出的方法优于已有基线.使用图形信息扩展需求为无监督的需求跟踪解决方案提供了新的见解,改进的跟踪链接性能验证了GeT2Trace的有用性和有效性.

Requirements tracing plays an important role to manage requirements and its related artifacts through the entire software life cycle.As manually creating such trace links is time-consuming and error-prone,some information retrieval(IR)based and machine learning(ML)based solutions have been proposed.Among them,unsupervised ML methods which do not require large labeled datasets are gaining more attention.Most of these solutions model the lexical and semantic information to resolve the problem.However,we find that existing approaches typically neglect the word co-occurrence distribution and word order information of the textual artifacts,which could provide extra indications for enhancing trace links.In this paper,we propose a novel approach,named GeT2Trace,which utilizes a graph mining-based expansion learning to enhance trace links recovery.The key idea is to exploit the word co-occurrence information and the word order information via graph network,and leverage them to learn a more comprehensive and accurate artifact representation.Evaluation is conducted on five public datasets,and the results show that our approach outperforms the state-of-the-art baselines.Expanding requirements with graphic information provide new insights into the unsupervised traceability solutions,and the improved trace links confirm the usefulness and effectiveness of GeT2Trace.