Achieving accuracy and scalability simultaneously in detecting application clones on Android markets
By Kai Chen, Peng Liu, Yingjun Zhang. 2014
316 citations recorded by the source corpus.
Subfields: Information Systems, Software, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Information Systems
- Gang Zhao
- Jeff Huang
- Chanchal Kumar Roy
- Kai Chen
- Michael Backes
- Hitesh Sajnani
- Cristina Videira Lopes
- Vaibhav Pratap Singh Saini
In Software
In Signal Processing
- Ting Su
- Gang Zhao
- Jeff Huang
- Yuting Chen
- Chanchal Kumar Roy
- Michael Backes
- Zhendong Su
- Jacques Klein
Citations in the corpus
- subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large Graphs (2016, methodology): “We acquired a dataset of 260 apps collected from the authors of a recent clone detection work, 3D-CFG [17].”
- Towards a Big Data Curated Benchmark of Inter-project Code Clones (2014, unknown): “For example, big data clone detection and clone search is used to find similar mobile applications [2], intelligently tag code snippets [3], find code examples [4], and so on.”
- Static analysis of android apps: A systematic literature review (2017, background): “[108], ClickRelease [113], CMA [116], Cortesi et al.”
- SourcererCC: Scaling Code Clone Detection to Big-Code (2015, methodology): “Large-scale clone detection can be used for mining library candidates [15], detecting similar mobile applications [6], license violation detection [20, 9], reverse engineering product lines [12, 9], finding the provenance of a component [8], and code search [19, 18].”
- DeepSim: deep learning code functional similarity (2018, background): “However, they either do not scale due to the complexity of graph isomorphism [13], or are imprecise due to the approximations made for improving scalability (e.g., mapping the subgraphs in PDG back to AST forest and comparing syntactical feature vectors extracted from AST [17]).”
- Guided, stochastic model-based GUI testing of Android apps (2017, background): “The key idea is to compute the “centroid” [11] of these N sequences, and take the sum of their Euclidean distances with the “centroid" as the diversity value of T .”
- Following Devil's Footprints: Cross-Platform Analysis of Potentially Harmful Libraries on Android and iOS (2016, methodology): “To compare two packages at the method level, LibFinder utilizes the centroid-based approach [5], which computes the geometric center (centroid) of a model derived from a method’s CFG (see Section II) to represent the method: two methods are considered to match each other if their centroids come…”
- Reliable Third-Party Library Detection in Android and its Security Applications (2016, methodology): “, by employing the concept of whitelisting package names to detect libraries within app code [14, 4, 5].”
- Stack Overflow Considered Harmful? The Impact of Copy&Paste on Android Application Security (2017, methodology): “[51] use Control Flow Graphs (CFG) in combination with opcodes to detect code clones in Android applications.”