most citedI know what leaked in your pocket: uncovering privacy leaks on Android Apps with Static Taint Analysis

68 citations · 73 across the 3 of their papers we have counts for

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cs.SE2024

Supporting Error Chains in Static Analysis for Precise Evaluation Results and Enhanced Usability

Anna-Katharina Wickert, Michael Schlichtig, Marvin Vogel +3

Context: Static analyses are well-established to aid in understanding bugs or vulnerabilities during the development process or in large-scale studies. A low false-positive rate is…

cs.SE2024

The Emergence of Large Language Models in Static Analysis: A First Look through Micro-Benchmarks

Ashwin Prasad Shivarpatna Venkatesh, Samkutty Sabu, Amir M. Mir +2

The application of Large Language Models (LLMs) in software engineering, particularly in static analysis tasks, represents a paradigm shift in the field. In this paper, we investig…

cs.SE20242 cited

Toward an Android Static Analysis Approach for Data Protection

Mugdha Khedkar, Eric Bodden

Android applications collecting data from users must protect it according to the current legal frameworks. Such data protection has become even more important since the European Un…

cs.SE2024

Symbol-Specific Sparsification of Interprocedural Distributive Environment Problems

Kadiray Karakaya, Eric Bodden

Previous work has shown that one can often greatly speed up static analysis by computing data flows not for every edge in the program's control-flow graph but instead only along de…

cs.SE20244 cited

TypeEvalPy: A Micro-benchmarking Framework for Python Type Inference Tools

Ashwin Prasad Shivarpatna Venkatesh, Samkutty Sabu, Jiawei Wang +3

In light of the growing interest in type inference research for Python, both researchers and practitioners require a standardized process to assess the performance of various type…

cs.SE201468 cited

I know what leaked in your pocket: uncovering privacy leaks on Android Apps with Static Taint Analysis

Li Li, Alexandre Bartel, Jacques Klein +6

Android applications may leak privacy data carelessly or maliciously. In this work we perform inter-component data-flow analysis to detect privacy leaks between components of Andro…