  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.
