HapTest: The Dynamic Analysis Framework for OpenHarmony
ArkTS is a new programming language dedicated to developing applications (apps) for the emerging OpenHarmony mobile operating system. Like other programs, apps developed with ArkTS suffer from bugs, leading to, e.g., crashes, or performance and security issues. Our community usually uses dynamic analysis to analyze the app's behavior and detect bugs. Unfortunately, a framework tailored for OpenHarmony apps dynamic analysis is not yet available for the developer community. To bridge this gap, we propose a new dynamic analysis framework named HapTest, which has been specifically designed to cope with OpenHarmony apps' original features. We make HapTest publicly available as an open-source project. Our HapTest has several fundamental dynamic analysis features (e.g., PTG, DataHub, etc.) that are ready to be reused by developers, and further customized to enable specific dynamic analysis, for instance, to detect malware or performance issues. Experiment results show that our HapTest achieves both high analysis coverage and high effectiveness. In addition, our HapTest is evaluated on the top 20 popular commercial apps from the OpenHarmony app market, each with at least millions of downloads. Our testing method revealed 26 previously unreported crashes in 11 out of the 20 applications, which demonstrates the practicality of HapTest.
Authors
- Jacques Klein (ORCID: https://orcid.org/0000-0003-4052-475X)
- Ting Su (ORCID: https://orcid.org/0000-0003-1628-9796)
- Farong Liu
- Mingyi Zhou (ORCID: https://orcid.org/0000-0003-3514-0372)
- Xiang Gao (ORCID: https://orcid.org/0000-0001-9895-4600)
- Yakun Zhang (ORCID: https://orcid.org/0009-0009-2377-3114)
- Bo Sun
- Li Li (ORCID: https://orcid.org/0000-0003-2990-1614)
Institutions
- University of Luxembourg (LU)
- Peking University (CN)
- Huawei Technologies (China) (CN)
- East China Normal University (CN)
- Beihang University (CN)
Publication Details
- Published
- 2025-06-23
- DOI
- https://doi.org/10.1145/3696630.3728565
- Citations
- 1
- Primary Topic
- Advanced Malware Detection Techniques
- Type
- article
- Field-Weighted Citation Impact
- 1.49
Funders
- National Key Research and Development Program of China