Soot: a Java bytecode optimization framework

By Raja Vallée-Rai, Phong Co, Etienne M. Gagnon, Laurie J. Hendren, Patrick Lam, V. Sundaresan. Conference of the Centre for Advanced Studies on Collaborative Research, 2010

1,398 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Hardware and Architecture

View Paper: https://www.semanticscholar.org/paper/ef9f26a22e3d62c7d05082744b09cbd330eef6da

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Swarat Chaudhuri
2. Koushik Sen
3. C. Jermaine
4. Vijayaraghavan Murali
5. Martin T. Vechev
6. Veselin Raychev
7. Zhendong Su
8. Giorgio Giacinto

In Hardware and Architecture

1. Swarat Chaudhuri
2. Yuting Chen
3. Zhendong Su
4. Koushik Sen
5. Thorsten Holz
6. David Wetherall
7. A. Aiken
8. Michael Bailey

Citations in the corpus

• Bayesian specification learning for finding API usage errors (2017, methodology): “Salento uses Soot [50] to implement symbolic execution and transform code in an Android app into our automaton model, TensorFlow [3] to implement the topic-conditioned RNN, and Scipy [30] to implement LDA.”
• MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Models (2016, methodology): “es with a much lower memory complexity in order to run the machine learning algorithms, because the number of dimensions of the features space where the classifier operates is remarkably reduced. Soot [52], which we use to extract call graphs, fails to analyze some apks. In fact, we were not able to ”
• Apposcopy: semantics-based detection of Android malware through static analysis (2014, methodology): “Our implementation consists of about 30,000 lines of Java and uses several publicly-available software such as Soot [35] and bddbddb [36].”
• Guided GUI testing of android apps with minimal restart and approximate learning (2013, methodology): “We instrument the dex file using Soot [110], a Java static analysis and instrumentation toolkit, to collect”
• Statistical Deobfuscation of Android Applications (2016, methodology): “DeGuard is implemened using Soot [38], a framework for static analysis of Java and Android applications.”
• Coverage-directed differential testing of JVM implementations (2016, methodology): “We utilize Soot [39] to mutate the seeds because it is a state-of-the-art Java analysis and transformation framework and provides rich APIs for parsing, transforming, and rewriting classfiles.”
• PScout: analyzing the Android permission specification (2012, methodology): “Soot - a Java bytecode optimization framework.”
• Security Analysis of Emerging Smart Home Applications (2016, methodology): “Since SmartApps compile to Java bytecode, we could have used an analysis framework like Soot to write a static analysis that computed overprivilege [31].”
• Brahmastra: Driving Apps to Test the Security of Third-Party Components (2014, methodology): “We use the Soot framework [29] to perform the bytecode rewriting that enables self execution.”
• Intriguing Properties of Adversarial ML Attacks in the Problem Space (2019, methodology): “We create a prototype of our novel problem-space attack (§III) using a combination of Python for the ML functionality and Java for the program analysis operations; in particular, to perform transplantations in the problem-space we rely on FlowDroid [9], which is based on Soot [68].”
• Detecting Similar Programs via The Weisfeiler-Leman Graph Kernel (2016, methodology): “For the graph construction, we extend Soot’s flow analysis to collect the node and edges of our graph per method, where we keep placeholder nodes for method calls.”
• FeatureSmith: Automatically Engineering Features for Malware Detection by Mining the Security Literature (2016, positive): “After collecting the .apk files for all the apps, we use dex2jar to decompile them to .jar files, and use Soot [49] to extract all the Android API calls.”
• Evaluating Analysis Tools for Android Apps: Status Quo and Robustness Against Obfuscation (2016, methodology): “to Java bytecode, and Soot [50] to transform and run code analyses.”
• Erays: Reverse Engineering Ethereum's Opaque Smart Contracts (2018, methodology): “In particular, our system design is largely influenced by Soot [49] and Marmot [25].”
• Malware Detection in Adversarial Settings: Exploiting Feature Evolutions and Confusions in Android Apps (2017, methodology): “We implement the process of extracting RTLD features using third-party static analysis frameworks, including Soot [40] and FlowDroid [36].”