一句话：提出突变驱动方法，利用模型检查与过程挖掘分析变异体杀死/存活，提升安全需求与状态机设计模型间的追溯精度。
需求追溯对确保软件与系统安全至关重要，但自动化需求追溯常因大量假阳性与真实链接混杂而面临低精度问题。论文提出一种突变驱动方法：主动生成许多看似正确的追溯目标，即状态机图变异体；在过程挖掘中利用模型检查自动验证安全需求属性是否在变异体中成立。若模型检查失败则变异体被杀死，否则存活。基于杀死-存活区分，作者设计相关性分析过程来识别追溯链接。在两个汽车系统中针对27条安全需求进行评估，与最新技术相比精度显著提升。
Traceability plays an essential role in assuring that software and systems are safe to use. Automated requirements traceability faces the low precision challenge due to a large number of false positives being returned and mingled with the true links. To overcome this challenge, we present a mutation-driven method built on the novel idea of proactively creating many seemingly correct tracing targets (i.e., mutants of a state machine diagram), and then exploiting model checking within process mining to automatically verify whether the safety requirement's properties hold in the mutants. A mutant is killed if its model checking fails; otherwise, it is survived. We leverage the underlying killed-survived distinction, and develop a correlation analysis procedure to identify the traceability links. Experimental evaluation results on two automotive systems with 27 safety requirements show considerable precision improvements compared with the state-of-the-art.
DOI 原文 ·
@inproceedings{paperbot5591,
title = {A novel approach to tracing safety requirements and state-based design models},
author = {Mounifah Alenazi and Nan Niu and Juha Savolainen},
booktitle = {ICSE},
year = {2020},
doi = {10.1145/3377811.3380332}
}