一句话：提出对比性细粒度度量与后处理/联合框架，过滤冗余边界条件，减少需工程师评估的分歧。
需求工程中，边界条件（BC）可刻画目标无法整体满足的分歧，但自动识别的 BC 数量庞大且冗余；现有泛化性（generality）度量过粗，难去除指向同一分歧的 BC，导致评估与消解成本高。本文引入 BC 的对比性（contrasty）细粒度度量：若两个 BC 对比，则它们刻画不同分歧。基于此，设计后处理框架 PPFc，在识别后生成对比性 BC 集合；进一步提出联合框架 JFc，将基于对比性的评估与 BC 识别交错，并引入偏向对比性 BC 的搜索偏置以剪枝。实验表明对比性度量显著减少推荐 BC 数量，揭示现有最优方法的大量 BC 冗余，JFc 也提升了对比性 BC 识别。
The boundary conditions (BCs) have shown great potential in requirements engineering because a BC captures the particular combination of circumstances, i.e., divergence, in which the goals of the requirement cannot be satisfied as a whole. Existing researches have attempted to automatically identify lots of BCs. Unfortunately, a large number of identified BCs make assessing and resolving divergences expensive. Existing methods adopt a coarse-grained metric, generality, to filter out less general BCs. However, the results still retain a large number of redundant BCs since a general BC potentially captures redundant circumstances that do not lead to a divergence. Furthermore, the likelihood of BC can be misled by redundant BCs resulting in costly repeatedly assessing and resolving divergences. In this paper, we present a fine-grained metric to filter out the redundant BCs. We first introduce the concept of contrasty of BC. Intuitively, if two BCs are contrastive, they capture different divergences. We argue that a set of contrastive BCs should be recommended to engineers, rather than a set of general BCs that potentially only indicates the same divergence. Then we design a post-processing framework (PPFc) to produce a set of contrastive BCs after identifying BCs. Experimental results show that the contrasty metric dramatically reduces the number of BCs recommended to engineers. Results also demonstrate that lots of BCs identified by the state-of-the-art method are redundant in most cases. Besides, to improve efficiency, we propose a joint framework (JFc) to interleave assessing based on the contrasty metric with identifying BCs. The primary intuition behind JFc is that it considers the search bias toward contrastive BCs during identifying BCs, thereby pruning the BCs capturing the same divergence. Experiments confirm the improvements of JFc in identifying contrastive BCs.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{abs-2103-02384,
title = {How to Identify Boundary Conditions with Contrasty Metric?},
author = {Weilin Luo and Hai Wan and Xiaotong Song and Binhao Yang and Hongzhen Zhong and Yin Chen},
booktitle = {ICSE},
year = {2021},
doi = {10.1109/icse43902.2021.00132}
}