一句话：提出分层贝叶斯网络方法，联合建模多样软件制品间关系，以提升可追溯性链接恢复效果。
可追溯性是现代软件开发过程中的基础环节，有助于保证程序正确与安全。人工建立追踪链接成本高昂，已有自动化方法通常基于相似度度量在文本软件制品对之间建立关系。然而这些方法往往只使用单一制品相似度，且无法同时建模多组异构开发制品之间的隐式与显式关系，因此效果受限。本文提出使用分层贝叶斯网络来改进可追溯性链接恢复，其核心思想是通过层次化概率模型整合并表达不同制品组之间的关系。摘要未给出具体实验设置与量化结果，但表明该方法旨在克服单相似度与关系建模不足的问题。
Traceability is a fundamental component of the modern software development process that helps to ensure properly functioning, secure programs. Due to the high cost of manually establishing trace links, researchers have developed automated approaches that draw relationships between pairs of textual software artifacts using similarity measures. However, the effectiveness of such techniques are often limited as they only utilize a single measure of artifact similarity and cannot simultaneously model (implicit and explicit) relationships across groups of diverse development artifacts.
@inproceedings{paperbot5590,
title = {Improving the effectiveness of traceability link recovery using hierarchical bayesian networks},
author = {Kevin Moran and David N. Palacio and Carlos Bernal-Cárdenas and Daniel McCrystal and Denys Poshyvanyk and Chris Shenefiel and Jeff Johnson},
booktitle = {ICSE},
year = {2020},
doi = {10.1145/3377811.3380418}
}