Traceability allows stakeholders to extract and comprehend the trace links among software artifacts introduced across the software life cycle, to provide significant support for software engineering tasks. Despite its proven benefits, software traceability is challenging to recover and maintain manually. Hence, plenty of approaches for automated traceability have been proposed. Most rely on textual similarities among software artifacts, such as those based on Information Retrieval (IR). However, artifacts in different abstraction levels usually have different textual descriptions, which can greatly hinder the performance of IR-based approaches (e.g., a requirement in natural language may have a small textual similarity to a Java class). In this work, we leverage the consensual biterms and transitive relationships (i.e., inner- and outer-transitive links) based on intermediate artifacts to improve IR-based traceability recovery. We first extract and filter biterms from all source, intermediate, and target artifacts. We then use the consensual biterms from the intermediate artifacts to extend the biterms of both source and target artifacts, and finally deduce outer and inner-transitive links to adjust text similarities between source and target artifacts. We conducted a comprehensive empirical evaluation based on five systems widely used in other literature to show that our approach can outperform four state-of-the-art approaches, and how its performance is affected by different conditions of source, intermediate, and target artifacts. The results indicate that our approach can outperform baseline approaches in AP over 15% and MAP over 10% on average.

翻译：可追溯性使利益相关者能够提取并理解软件生命周期中引入的软件工件之间的追踪链接，从而为软件工程任务提供重要支持。尽管其益处已得到证实，但软件可追溯性的恢复与维护仍然具有挑战性。因此，研究人员提出了多种自动化可追溯性方法，其中大多依赖软件工件间的文本相似性，例如基于信息检索（IR）的方法。然而，不同抽象级别的工件通常具有不同的文本描述，这可能严重阻碍基于IR的方法的性能（例如，自然语言描述的需求可能与Java类仅有极低的文本相似性）。在本研究中，我们利用中间工件中的共识双词和传递关系（即内部传递链接与外部传递链接）来改进基于IR的可追溯性恢复。首先，我们从所有源工件、中间工件和目标工件中提取并过滤双词。随后，使用中间工件的共识双词扩展源工件与目标工件的双词集合，最后推导外部与内部传递链接以调整源工件与目标工件间的文本相似度。基于其他文献中广泛使用的五个系统进行全面的实证评估，结果表明我们的方法在平均精确率（AP）上平均提升超过15%，平均平均精确率（MAP）上平均提升超过10%，优于四种现有先进方法，并揭示了源工件、中间工件和目标工件不同条件对其性能的影响。