CARE: Cascading Impact-Aware Compliance Test Suite Evolution under Regulatory Changes
Zhiyi Xue, Xiaohong Chen, Min Zhang
Abstract
In response to frequent changes in regulatory rules, this paper proposes CARE, a cascading impact-aware framework for automated compliance testing evolution. Existing approaches often suffer from over-reuse or missed updates because they treat rule changes in isolation and ignore complex inter-dependencies across testing artifacts. This paper highlights cascading impact propagation as a central challenge in regulation-driven test maintenance and shows that shifting from isolated rule handling to cascading impact-aware evolution is essential for achieving both high test quality and maintenance efficiency. Specifically, our CARE framework addresses this challenge by constructing a unified four-layer cascading relation model spanning Rule-Requirement-Scenario-Test Case, enabling fine-grained traceability across abstraction levels. By explicitly modeling how rule changes propagate and amplify along this chain, the framework precisely identifies impacted scenarios and test cases that need updating, while safely maximizing the reuse of unaffected ones. Experiments conducted on real-world compliance testing tasks across multiple domains show that CARE achieves an average F1 of 90.3% on updated test suites, outperforming existing methods by up to 164% and approaching expert-level effectiveness. Ablation studies further demonstrate that explicit cascading impact modeling and handling are key contributors to these improvements. In addition, CARE substantially reduces manual effort and improves test maintenance efficiency, and indicates strong cross-domain generalization.
Ask about this paper
Ask your agent about it.
Lune has read the top-tier papers around this one, so every answer names the papers it rests on.
Your agent calls
Lunesearch_papers
Free to start. No credit card required.
Terminal
Install the CLIlune papers get 2f463183-3fdc-4962-8f34-ecb7ce340626Related papers
- Identify and Update Test Cases When Production Code Changes: A Transformer-Based ApproachXing Hu, Zhuang Liu, Xin Xia, Zhongxin Liu et al.ASE 2023 · 15 citations
- Characterizing and Repairing Obsolete Android GUI Tests under UI EvolutionShiwen Song, Yiheng Xiong, Wenbo Guo, Manqi Sun et al.ISSTA 2026
- Just Old Wine in a New Bottle: Improving Vulnerability Repair Robustness to Combat Homologous but Heterogeneous VulnerabilitiesXin Peng, Bo Lin, Yihao Qin, Jing Wang et al.ISSTA 2026
- Co-evolving code with evolving metamodelsDjamel Eddine Khelladi, Benoît Combemale, Mathieu Acher, Olivier Barais et al.ICSE 2020 · 11 citations
- Putting them under microscope: a fine-grained approach for detecting redundant test cases in natural languageZhiyuan Chang, Mingyang Li, Junjie Wang, Qing Wang et al.FSE 2022 · 3 citations
