一句话：TRIAD 利用中间工件的共识 Biterm 与内外传递链接增强 IR 可追溯性恢复，优于四种 SOTA 方法。
软件可追溯性链接跨越生命周期各类工件，但人工恢复与维护困难。现有自动方法多依赖文本相似度，然而不同抽象层级工件的文本描述差异大，常使 IR 方法失效。TRIAD 从源、中间和目标工件抽取并过滤 Biterm，用中间工件的共识 Biterm 扩展源、目标文本，再推导内传递与外传递链接来调整源-目标相似度。在五个常用系统上评估，与四种最新方法相比，Average Precision 平均提升超过 15%，Mean Average Precision 平均提升超过 10%。
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 enrich the texts 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 in Average Precision over 15% and Mean Average Precision over 10% on average.
@inproceedings{paperbot11346,
title = {TRIAD: Automated Traceability Recovery based on Biterm-enhanced Deduction of Transitive Links among Artifacts},
author = {Hui Gao and Hongyu Kuang and Wesley K. G. Assunção and Christoph Mayr-Dorn and Guoping Rong and He Zhang and Xiaoxing Ma and Alexander Egyed},
booktitle = {ICSE},
year = {2024},
doi = {10.1145/3597503.3639164}
}