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P2 A combinatorial approach for exposing off-nominal behaviors

一种暴露非标称行为的组合方法
ICSE Proceedings of the 40th International Conference on Software Engineering2018匹配度 1/10 · 该论文研究需求工程中的组合测试方法,用于从自然语言需求中暴露非标称行为,属于软件测试与安全关键系统领域;与用户关注的静态分析、LLM/Agentic 程序分析、浮点程序分析、贝叶斯程序分析及形式化验证均无直接关联。引用 14 inbox
Kaushik Madala, Hyunsook Do, Daniel Aceituna
crossref: 2026-09-12

AI 速览

一句话:提出 Combi-CCM:用结构化需求模式和 IPOG 组合生成暴露非标称行为,降低人工与时间成本。

嵌入式与安全关键系统中,非标称行为(ONB)是重要隐患。已有基于模型的方法通过分析自然语言需求文档来暴露 ONB,但需要大量人工和时间。本文提出组合式因果组件模型 Combi-CCM,利用结构化需求模式与 IPOG 算法生成组合,以减少人工和时间开销。作者使用多份需求文档开展实证研究,结果表明该方法能够减少人工与时间,同时保持与对照技术相同的 ONB 暴露能力。

问题
嵌入式与安全关键系统中的非标称行为(ONB)问题突出,而基于模型的需求分析虽能暴露 ONB,但人工与时间开销较大。
方法
提出 Combi-CCM,结合结构化需求模式与 IPOG 算法生成的组合来暴露 ONB。
评估
使用多份需求文档进行实证研究,并与对照技术比较,结果显示可减少人工与时间且保持相当的 ONB 暴露能力。
原文摘要(Abstract)

Off-nominal behaviors (ONBs) have been a major concern in the areas of embedded systems and safety-critical systems. To address ONB problems, some researchers have proposed model-based approaches that can expose ONBs by analyzing natural language requirements documents. While these approaches produced promising results, they require a lot of human effort and time. In this paper, to reduce human effort and time, we propose a combinatorial-based approach, Combinatorial Causal Component Model (Combi-CCM), which uses structured requirements patterns and combinations generated using the IPOG algorithm. We conducted an empirical study using several requirements documents to evaluate our approach, and our results indicate that the proposed approach can reduce human effort and time while maintaining the same ONB exposure ability obtained by the control techniques.

链接与引用

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BibTeX
@inproceedings{paperbot4529,
  title = {A combinatorial approach for exposing off-nominal behaviors},
  author = {Kaushik Madala and Hyunsook Do and Daniel Aceituna},
  booktitle = {ICSE},
  year = {2018},
  doi = {10.1145/3180155.3180204}
}