一句话：提出 ENRL 方法，用机器学习分类器估计追溯恢复候选列表中剩余正链接的数量。
建立软件制品间的追溯链接极其繁琐、易错且代价高昂；自动追溯恢复虽能产生候选链接，但通常给出很长的排序列表，需分析人员人工检查。本文提出 ENRL（Estimation of the Number of Remaining Links）方法，利用机器学习分类器估计基于自然语言处理（NLP）的恢复方法所产生候选链接排序列表中剩余正链接的数量。作者在来自工业界与学术界的三个数据集上评估 ENRL，涉及需求、用例、设计文档、源代码和测试用例等多种制品，并比较多种 ML 分类器与 NLP 技术。结果表明：特定估计模型能准确估计剩余正链接数；估计精度取决于 NLP 技术的选择；单变量估计模型优于多变量模型。
Although very important in software engineering, establishing traceability links between software artifacts is extremely tedious, error-prone, and it requires significant effort. Even when approaches for automated traceability recovery exist, these provide the requirements analyst with a, usually very long, ranked list of candidate links that needs to be manually inspected. In this paper we introduce an approach called Estimation of the Number of Remaining Links (ENRL) which aims at estimating, via Machine Learning (ML) classifiers, the number of remaining positive links in a ranked list of candidate traceability links produced by a Natural Language Processing techniques-based recovery approach. We have evaluated the accuracy of the ENRL approach by considering several ML classifiers and NLP techniques on three datasets from industry and academia, and concerning traceability links among different kinds of software artifacts including requirements, use cases, design documents, source code, and test cases. Results from our study indicate that: (i) specific estimation models are able to provide accurate estimates of the number of remaining positive links; (ii) the estimation accuracy depends on the choice of the NLP technique, and (iii) univariate estimation models outperform multivariate ones.
DOI 原文 ·
@inproceedings{paperbot4769,
title = {Estimating the number of remaining links in traceability recovery (journal-first abstract)},
author = {Davide Falessi and Massimiliano Di Penta and Gerardo Canfora and Giovanni Cantone},
booktitle = {ASE},
year = {2018},
doi = {10.1145/3238147.3241982}
}