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Android devices, handling sensitive data like call records and text messages, are prone to privacy breaches. Existing information flow tracking systems face difficulties in detecting these breaches due to two main challenges: the multi-layered Android platform using different programming languages (Java and C/C++), and the complex, event-driven execution flow of Android apps that complicates tracking, especially across these language barriers. Our system, DryJIN, addresses this by effectively tracking information flow within and across both Java and native modules. Utilizing symbolic execution for native code data flows and integrating it with Java data flows, DryJIN enhances existing static analysis techniques (Argus-SAF, JuCify, and FlowDroid) to cover previously unaddressed information flow patterns. We validated DryJIN ’s effectiveness through a comprehensive evaluation on over 168k apps, including malware and real-world apps, demonstrating its superiority over current state-of-the-art methods.
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Automatic inference of Java-to-swift translation rules for porting mobile applications
A native cross-platform mobile app has multiple platform-specific implementations. Typically, an app is developed for one platform and then ported to the remaining ones. Translating an app from one language (e.g., Java) to another (e.g., Swift) by hand is tedious and error-prone, while automated translators either require manually defined translation rules or focus on translating APIs. To automate the translation of native cross-platform apps, we present J2SINFERER, a novel approach that iteratively infers syntactic transformation rules and API mappings from Java to Swift. Given a software corpus in both languages, J2SLNFERER first identifies the syntactically equivalent code based on braces and string similarity. For each pair of similar code segments, J2SLNFERER then creates syntax trees of both languages, leveraging the minimalist domain knowledge of language correspondence (e.g., operators and markers) to iteratively align syntax tree nodes, and to infer both syntax and API mapping rules. J2SLNFERER represents inferred rules as string templates, stored in a database, to translate code from Java to Swift. We evaluated J2SLNFERER with four applications, using one part of the data to infer translation rules, and the other part to apply the rules. With 76% in-project accuracy and 65% cross-project accuracy, J2SLNFERER outperforms in accuracy j2swift, a state-of-the-art Java-to-Swift conversion tool. As native cross-platform mobile apps grow in popularity, J2SLNFERER can shorten their time to market by automating the tedious and error prone task of source-to-source translation.
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- Award ID(s):
- 1717065
- PAR ID:
- 10096814
- Date Published:
- Journal Name:
- Proceedings of the 5th International Conference on Mobile Software Engineering and Systems
- Page Range / eLocation ID:
- 180 to 190
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3197231.3197240
