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：摘要：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各类制品间的追踪链接，为软件工程任务提供重要支持。尽管已证实其价值，但软件可追溯性的人工恢复与维护仍具挑战性。为此，学界提出了多种自动化可追溯性方法，其中多数方法依赖于软件制品间的文本相似性，例如基于信息检索（IR）的方法。然而，不同抽象层级的制品往往具有差异化的文本描述，这会严重制约基于IR方法的性能（如自然语言描述的需求与Java类之间的文本相似性可能极低）。本研究利用基于中间制品的共识性双词（consensual biterms）和传递关系（即内部与外部传递链接）改进基于IR的可追溯性恢复。我们首先从所有源制品、中间制品和目标制品中提取并过滤双词，随后采用中间制品中的共识性双词扩展源制品与目标制品的双词集合，最终通过推导外部与内部传递链接调整源制品与目标制品间的文本相似度。基于文献中广泛采用的五个系统开展全面实证评估，结果表明本方法在性能上优于四种前沿方法，且不同条件下源制品、中间制品与目标制品的特性会影响其表现。实验数据表明，本方法在AP指标上平均提升超过15%，在MAP指标上平均提升超过10%。